[{"publisher":"IEEE","year":"2023","issue":"12","article_processing_charge":"Yes (in subscription journal)","intvolume":"        45","language":[{"iso":"eng"}],"acknowledgement":"The work of Maria-Jose Jimenez, Eduardo Paluzo-Hidalgo and Manuel Soriano-Trigueros was supported in part by the Spanish grant Ministerio de Ciencia e Innovacion under Grants TED2021-129438B-I00 and PID2019-107339GB-I00, and in part by REXASI-PRO H-EU project, call HORIZON-CL4-2021-HUMAN-01-01 under Grant 101070028. The work of\r\nMaria-Jose Jimenez was supported by a grant of Convocatoria de la Universidad de Sevilla para la recualificacion del sistema universitario español, 2021-23, funded by the European Union, NextGenerationEU. The work of Vidit Nanda was supported in part by EPSRC under Grant EP/R018472/1 and in part by US AFOSR under Grant FA9550-22-1-0462. \r\nWe are grateful to the team of GUDHI and TEASPOON developers, for their work and their support. We are also grateful to Streamlit for providing extra resources to deploy the web app\r\nonline on Streamlit community cloud. We thank the anonymous referees for their helpful suggestions.","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","department":[{"_id":"HeEd"}],"scopus_import":"1","status":"public","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","citation":{"mla":"Ali, Dashti, et al. “A Survey of Vectorization Methods in Topological Data Analysis.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 45, no. 12, IEEE, 2023, pp. 14069–80, doi:<a href=\"https://doi.org/10.1109/tpami.2023.3308391\">10.1109/tpami.2023.3308391</a>.","ieee":"D. Ali, A. Asaad, M.-J. Jimenez, V. Nanda, E. Paluzo-Hidalgo, and M. Soriano Trigueros, “A survey of vectorization methods in topological data analysis,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 45, no. 12. IEEE, pp. 14069–14080, 2023.","ama":"Ali D, Asaad A, Jimenez M-J, Nanda V, Paluzo-Hidalgo E, Soriano Trigueros M. A survey of vectorization methods in topological data analysis. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2023;45(12):14069-14080. doi:<a href=\"https://doi.org/10.1109/tpami.2023.3308391\">10.1109/tpami.2023.3308391</a>","ista":"Ali D, Asaad A, Jimenez M-J, Nanda V, Paluzo-Hidalgo E, Soriano Trigueros M. 2023. A survey of vectorization methods in topological data analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(12), 14069–14080.","chicago":"Ali, Dashti, Aras Asaad, Maria-Jose Jimenez, Vidit Nanda, Eduardo Paluzo-Hidalgo, and Manuel Soriano Trigueros. “A Survey of Vectorization Methods in Topological Data Analysis.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE, 2023. <a href=\"https://doi.org/10.1109/tpami.2023.3308391\">https://doi.org/10.1109/tpami.2023.3308391</a>.","short":"D. Ali, A. Asaad, M.-J. Jimenez, V. Nanda, E. Paluzo-Hidalgo, M. Soriano Trigueros, IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2023) 14069–14080.","apa":"Ali, D., Asaad, A., Jimenez, M.-J., Nanda, V., Paluzo-Hidalgo, E., &#38; Soriano Trigueros, M. (2023). A survey of vectorization methods in topological data analysis. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE. <a href=\"https://doi.org/10.1109/tpami.2023.3308391\">https://doi.org/10.1109/tpami.2023.3308391</a>"},"quality_controlled":"1","external_id":{"isi":["001104973300002"]},"volume":45,"date_created":"2024-01-08T09:59:46Z","page":"14069-14080","author":[{"first_name":"Dashti","full_name":"Ali, Dashti","last_name":"Ali"},{"first_name":"Aras","full_name":"Asaad, Aras","last_name":"Asaad"},{"full_name":"Jimenez, Maria-Jose","first_name":"Maria-Jose","last_name":"Jimenez"},{"last_name":"Nanda","first_name":"Vidit","full_name":"Nanda, Vidit"},{"last_name":"Paluzo-Hidalgo","full_name":"Paluzo-Hidalgo, Eduardo","first_name":"Eduardo"},{"last_name":"Soriano Trigueros","id":"15ebd7cf-15bf-11ee-aebd-bb4bb5121ea8","full_name":"Soriano Trigueros, Manuel","first_name":"Manuel","orcid":"0000-0003-2449-1433"}],"_id":"14739","day":"01","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"title":"A survey of vectorization methods in topological data analysis","date_published":"2023-12-01T00:00:00Z","article_type":"original","date_updated":"2025-09-09T14:08:56Z","publication_identifier":{"issn":["0162-8828"],"eissn":["1939-3539"]},"file":[{"content_type":"application/pdf","file_id":"14740","date_updated":"2024-01-08T10:09:14Z","file_name":"2023_IEEEToP_Ali.pdf","creator":"dernst","date_created":"2024-01-08T10:09:14Z","success":1,"checksum":"465c28ef0b151b4b1fb47977ed5581ab","access_level":"open_access","relation":"main_file","file_size":2370988}],"month":"12","abstract":[{"lang":"eng","text":"Attempts to incorporate topological information in supervised learning tasks have resulted in the creation of several techniques for vectorizing persistent homology barcodes. In this paper, we study thirteen such methods. Besides describing an organizational framework for these methods, we comprehensively benchmark them against three well-known classification tasks. Surprisingly, we discover that the best-performing method is a simple vectorization, which consists only of a few elementary summary statistics. Finally, we provide a convenient web application which has been designed to facilitate exploration and experimentation with various vectorization methods."}],"oa":1,"ddc":["000"],"keyword":["Applied Mathematics","Artificial Intelligence","Computational Theory and Mathematics","Computer Vision and Pattern Recognition","Software"],"file_date_updated":"2024-01-08T10:09:14Z","doi":"10.1109/tpami.2023.3308391","publication_status":"published","has_accepted_license":"1","oa_version":"Published Version","type":"journal_article","isi":1},{"issue":"2","publisher":"Institute of Electrical and Electronics Engineers","year":"2020","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_place":"repository","article_processing_charge":"No","intvolume":"        44","scopus_import":"1","external_id":{"arxiv":["1909.06397"]},"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","OA_type":"green","status":"public","quality_controlled":"1","citation":{"apa":"Sommer, S., &#38; Bronstein, A. M. (2020). Horizontal flows and manifold stochastics in geometric deep learning. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/tpami.2020.2994507\">https://doi.org/10.1109/tpami.2020.2994507</a>","short":"S. Sommer, A.M. Bronstein, IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (2020) 811–822.","ista":"Sommer S, Bronstein AM. 2020. Horizontal flows and manifold stochastics in geometric deep learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(2), 811–822.","chicago":"Sommer, Stefan, and Alex M. Bronstein. “Horizontal Flows and Manifold Stochastics in Geometric Deep Learning.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and Electronics Engineers, 2020. <a href=\"https://doi.org/10.1109/tpami.2020.2994507\">https://doi.org/10.1109/tpami.2020.2994507</a>.","ama":"Sommer S, Bronstein AM. Horizontal flows and manifold stochastics in geometric deep learning. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2020;44(2):811-822. doi:<a href=\"https://doi.org/10.1109/tpami.2020.2994507\">10.1109/tpami.2020.2994507</a>","mla":"Sommer, Stefan, and Alex M. Bronstein. “Horizontal Flows and Manifold Stochastics in Geometric Deep Learning.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 44, no. 2, Institute of Electrical and Electronics Engineers, 2020, pp. 811–22, doi:<a href=\"https://doi.org/10.1109/tpami.2020.2994507\">10.1109/tpami.2020.2994507</a>.","ieee":"S. Sommer and A. M. Bronstein, “Horizontal flows and manifold stochastics in geometric deep learning,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 44, no. 2. Institute of Electrical and Electronics Engineers, pp. 811–822, 2020."},"volume":44,"author":[{"first_name":"Stefan","full_name":"Sommer, Stefan","last_name":"Sommer"},{"last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","orcid":"0000-0001-9699-8730","first_name":"Alexander","full_name":"Bronstein, Alexander"}],"page":"811-822","_id":"18228","date_created":"2024-10-08T12:55:23Z","day":"01","article_type":"original","date_published":"2020-02-01T00:00:00Z","publication_identifier":{"issn":["0162-8828"],"eissn":["1939-3539"]},"date_updated":"2024-10-15T06:56:47Z","title":"Horizontal flows and manifold stochastics in geometric deep learning","month":"02","abstract":[{"lang":"eng","text":"We introduce two constructions in geometric deep learning for 1) transporting orientation-dependent convolutional filters over a manifold in a continuous way and thereby defining a convolution operator that naturally incorporates the rotational effect of holonomy; and 2) allowing efficient evaluation of manifold convolution layers by sampling manifold valued random variables that center around a weighted diffusion mean. Both methods are inspired by stochastics on manifolds and geometric statistics, and provide examples of how stochastic methods – here horizontal frame bundle flows and non-linear bridge sampling schemes, can be used in geometric deep learning. We outline the theoretical foundation of the two methods, discuss their relation to Euclidean deep networks and existing methodology in geometric deep learning, and establish important properties of the proposed constructions."}],"oa":1,"extern":"1","arxiv":1,"publication_status":"published","doi":"10.1109/tpami.2020.2994507","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1909.06397","open_access":"1"}],"type":"journal_article","oa_version":"Preprint"},{"year":"2020","publisher":"Institute of Electrical and Electronics Engineers","issue":"10","article_processing_charge":"No","intvolume":"        42","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":"1","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","status":"public","OA_type":"closed access","quality_controlled":"1","citation":{"ieee":"A. Zabatani <i>et al.</i>, “Intel® RealSense<sup>TM</sup> SR300 coded light depth camera,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 42, no. 10. Institute of Electrical and Electronics Engineers, pp. 2333–2345, 2020.","mla":"Zabatani, Aviad, et al. “Intel® RealSense<sup>TM</sup> SR300 Coded Light Depth Camera.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 42, no. 10, Institute of Electrical and Electronics Engineers, 2020, pp. 2333–45, doi:<a href=\"https://doi.org/10.1109/tpami.2019.2915841\">10.1109/tpami.2019.2915841</a>.","ama":"Zabatani A, Surazhsky V, Sperling E, et al. Intel® RealSense<sup>TM</sup> SR300 coded light depth camera. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2020;42(10):2333-2345. doi:<a href=\"https://doi.org/10.1109/tpami.2019.2915841\">10.1109/tpami.2019.2915841</a>","ista":"Zabatani A, Surazhsky V, Sperling E, Moshe SB, Menashe O, Silver DH, Karni Z, Bronstein AM, Bronstein MK, Kimmel R. 2020. Intel® RealSense<sup>TM</sup> SR300 coded light depth camera. IEEE Transactions on Pattern Analysis and Machine Intelligence. 42(10), 2333–2345.","chicago":"Zabatani, Aviad, Vitaly Surazhsky, Erez Sperling, Sagi Ben Moshe, Ohad Menashe, David H. Silver, Zachi Karni, Alex M. Bronstein, Michael K Bronstein, and Ron Kimmel. “Intel® RealSense<sup>TM</sup> SR300 Coded Light Depth Camera.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and Electronics Engineers, 2020. <a href=\"https://doi.org/10.1109/tpami.2019.2915841\">https://doi.org/10.1109/tpami.2019.2915841</a>.","apa":"Zabatani, A., Surazhsky, V., Sperling, E., Moshe, S. B., Menashe, O., Silver, D. H., … Kimmel, R. (2020). Intel® RealSense<sup>TM</sup> SR300 coded light depth camera. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/tpami.2019.2915841\">https://doi.org/10.1109/tpami.2019.2915841</a>","short":"A. Zabatani, V. Surazhsky, E. Sperling, S.B. Moshe, O. Menashe, D.H. Silver, Z. Karni, A.M. Bronstein, M.K. Bronstein, R. Kimmel, IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (2020) 2333–2345."},"external_id":{"pmid":["31094683"]},"volume":42,"date_created":"2024-10-08T13:04:18Z","author":[{"full_name":"Zabatani, Aviad","first_name":"Aviad","last_name":"Zabatani"},{"first_name":"Vitaly","full_name":"Surazhsky, Vitaly","last_name":"Surazhsky"},{"full_name":"Sperling, Erez","first_name":"Erez","last_name":"Sperling"},{"last_name":"Moshe","first_name":"Sagi Ben","full_name":"Moshe, Sagi Ben"},{"full_name":"Menashe, Ohad","first_name":"Ohad","last_name":"Menashe"},{"last_name":"Silver","full_name":"Silver, David H.","first_name":"David H."},{"last_name":"Karni","full_name":"Karni, Zachi","first_name":"Zachi"},{"orcid":"0000-0001-9699-8730","full_name":"Bronstein, Alexander","first_name":"Alexander","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6"},{"first_name":"Michael K","full_name":"Bronstein, Michael K","last_name":"Bronstein"},{"last_name":"Kimmel","full_name":"Kimmel, Ron","first_name":"Ron"}],"page":"2333-2345","_id":"18245","day":"01","pmid":1,"title":"Intel® RealSense™ SR300 coded light depth camera","article_type":"original","date_published":"2020-10-01T00:00:00Z","publication_identifier":{"issn":["0162-8828"],"eissn":["1939-3539"]},"date_updated":"2024-10-15T09:40:01Z","month":"10","abstract":[{"lang":"eng","text":"Intel® RealSense™ SR300 is a depth camera capable of providing a VGA-size depth map at 60 fps and 0.125mm depth resolution. In addition, it outputs an infrared VGA-resolution image and a 1080p color texture image at 30 fps. SR300 form-factor enables it to be integrated into small consumer products and as a front facing camera in laptops and Ultrabooks™. The SR300 depth camera is based on a coded-light technology where triangulation between projected patterns and images captured by a dedicated sensor is used to produce the depth map. Each projected line is coded by a special temporal optical code, that enables a dense depth map reconstruction from its reflection. The solid mechanical assembly of the camera allows it to stay calibrated throughout temperature and pressure changes, drops, and hits. In addition, active dynamic control maintains a calibrated depth output. An extended API LibRS released with the camera allows developers to integrate the camera in various applications. Algorithms for 3D scanning, facial analysis, hand gesture recognition, and tracking are within reach for applications using the SR300. In this paper, we describe the underlying technology, hardware, and algorithms of the SR300, as well as its calibration procedure, and outline some use cases. We believe that this paper will provide a full case study of a mass-produced depth sensing product and technology."}],"extern":"1","doi":"10.1109/tpami.2019.2915841","publication_status":"published","oa_version":"None","type":"journal_article"},{"publication_status":"published","doi":"10.1109/tpami.2018.2857768","arxiv":1,"oa_version":"Preprint","isi":1,"type":"journal_article","main_file_link":[{"url":"https://arxiv.org/abs/1707.00600","open_access":"1"}],"abstract":[{"lang":"eng","text":"Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it."}],"oa":1,"title":"Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly","date_published":"2019-09-01T00:00:00Z","article_type":"original","publication_identifier":{"issn":["0162-8828"],"eissn":["1939-3539"]},"date_updated":"2024-12-11T11:49:58Z","month":"09","day":"01","volume":41,"date_created":"2019-06-11T14:05:59Z","author":[{"full_name":"Xian, Yongqin","first_name":"Yongqin","last_name":"Xian"},{"first_name":"Christoph","full_name":"Lampert, Christoph","orcid":"0000-0002-4561-241X","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Schiele","first_name":"Bernt","full_name":"Schiele, Bernt"},{"full_name":"Akata, Zeynep","first_name":"Zeynep","last_name":"Akata"}],"page":"2251 - 2265","_id":"6554","department":[{"_id":"ChLa"}],"scopus_import":"1","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","status":"public","quality_controlled":"1","citation":{"ieee":"Y. Xian, C. Lampert, B. Schiele, and Z. Akata, “Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 41, no. 9. Institute of Electrical and Electronics Engineers, pp. 2251–2265, 2019.","mla":"Xian, Yongqin, et al. “Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 41, no. 9, Institute of Electrical and Electronics Engineers, 2019, pp. 2251–65, doi:<a href=\"https://doi.org/10.1109/tpami.2018.2857768\">10.1109/tpami.2018.2857768</a>.","ama":"Xian Y, Lampert C, Schiele B, Akata Z. Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2019;41(9):2251-2265. doi:<a href=\"https://doi.org/10.1109/tpami.2018.2857768\">10.1109/tpami.2018.2857768</a>","ista":"Xian Y, Lampert C, Schiele B, Akata Z. 2019. Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(9), 2251–2265.","chicago":"Xian, Yongqin, Christoph Lampert, Bernt Schiele, and Zeynep Akata. “Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and Electronics Engineers, 2019. <a href=\"https://doi.org/10.1109/tpami.2018.2857768\">https://doi.org/10.1109/tpami.2018.2857768</a>.","short":"Y. Xian, C. Lampert, B. Schiele, Z. Akata, IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (2019) 2251–2265.","apa":"Xian, Y., Lampert, C., Schiele, B., &#38; Akata, Z. (2019). Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/tpami.2018.2857768\">https://doi.org/10.1109/tpami.2018.2857768</a>"},"external_id":{"isi":["000480343900015"],"arxiv":["1707.00600"]},"publisher":"Institute of Electrical and Electronics Engineers","year":"2019","issue":"9","article_processing_charge":"No","intvolume":"        41","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"issue":"7","publisher":"IEEE","year":"2018","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","language":[{"iso":"eng"}],"intvolume":"        40","article_processing_charge":"No","scopus_import":"1","department":[{"_id":"VlKo"}],"external_id":{"isi":["000434294800010"],"arxiv":["1508.07902"]},"quality_controlled":"1","citation":{"ama":"Shekhovtsov A, Swoboda P, Savchynskyy B. Maximum persistency via iterative relaxed inference with graphical models. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2018;40(7):1668-1682. doi:<a href=\"https://doi.org/10.1109/TPAMI.2017.2730884\">10.1109/TPAMI.2017.2730884</a>","mla":"Shekhovtsov, Alexander, et al. “Maximum Persistency via Iterative Relaxed Inference with Graphical Models.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 40, no. 7, IEEE, 2018, pp. 1668–82, doi:<a href=\"https://doi.org/10.1109/TPAMI.2017.2730884\">10.1109/TPAMI.2017.2730884</a>.","ieee":"A. Shekhovtsov, P. Swoboda, and B. Savchynskyy, “Maximum persistency via iterative relaxed inference with graphical models,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 40, no. 7. IEEE, pp. 1668–1682, 2018.","short":"A. Shekhovtsov, P. Swoboda, B. Savchynskyy, IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (2018) 1668–1682.","apa":"Shekhovtsov, A., Swoboda, P., &#38; Savchynskyy, B. (2018). Maximum persistency via iterative relaxed inference with graphical models. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE. <a href=\"https://doi.org/10.1109/TPAMI.2017.2730884\">https://doi.org/10.1109/TPAMI.2017.2730884</a>","chicago":"Shekhovtsov, Alexander, Paul Swoboda, and Bogdan Savchynskyy. “Maximum Persistency via Iterative Relaxed Inference with Graphical Models.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE, 2018. <a href=\"https://doi.org/10.1109/TPAMI.2017.2730884\">https://doi.org/10.1109/TPAMI.2017.2730884</a>.","ista":"Shekhovtsov A, Swoboda P, Savchynskyy B. 2018. Maximum persistency via iterative relaxed inference with graphical models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40(7), 1668–1682."},"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","status":"public","volume":40,"_id":"703","page":"1668-1682","author":[{"full_name":"Shekhovtsov, Alexander","first_name":"Alexander","last_name":"Shekhovtsov"},{"id":"446560C6-F248-11E8-B48F-1D18A9856A87","last_name":"Swoboda","full_name":"Swoboda, Paul","first_name":"Paul"},{"full_name":"Savchynskyy, Bogdan","first_name":"Bogdan","last_name":"Savchynskyy"}],"date_created":"2018-12-11T11:48:01Z","corr_author":"1","day":"01","date_updated":"2026-04-16T09:54:52Z","publication_identifier":{"issn":["0162-8828"]},"date_published":"2018-07-01T00:00:00Z","title":"Maximum persistency via iterative relaxed inference with graphical models","month":"07","oa":1,"abstract":[{"text":"We consider the NP-hard problem of MAP-inference for undirected discrete graphical models. We propose a polynomial time and practically efficient algorithm for finding a part of its optimal solution. Specifically, our algorithm marks some labels of the considered graphical model either as (i) optimal, meaning that they belong to all optimal solutions of the inference problem; (ii) non-optimal if they provably do not belong to any solution. With access to an exact solver of a linear programming relaxation to the MAP-inference problem, our algorithm marks the maximal possible (in a specified sense) number of labels. We also present a version of the algorithm, which has access to a suboptimal dual solver only and still can ensure the (non-)optimality for the marked labels, although the overall number of the marked labels may decrease. We propose an efficient implementation, which runs in time comparable to a single run of a suboptimal dual solver. Our method is well-scalable and shows state-of-the-art results on computational benchmarks from machine learning and computer vision.","lang":"eng"}],"arxiv":1,"publication_status":"published","doi":"10.1109/TPAMI.2017.2730884","publist_id":"6992","type":"journal_article","main_file_link":[{"url":"https://arxiv.org/abs/1508.07902","open_access":"1"}],"isi":1,"oa_version":"Preprint"},{"publisher":"IEEE","year":"2015","issue":"9","intvolume":"        37","article_processing_charge":"No","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"scopus_import":"1","citation":{"ista":"Sprechmann P, Bronstein AM, Sapiro G. 2015. Learning efficient sparse and low rank models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(9), 1821–1833.","chicago":"Sprechmann, P., Alex M. Bronstein, and G. Sapiro. “Learning Efficient Sparse and Low Rank Models.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE, 2015. <a href=\"https://doi.org/10.1109/tpami.2015.2392779\">https://doi.org/10.1109/tpami.2015.2392779</a>.","short":"P. Sprechmann, A.M. Bronstein, G. Sapiro, IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (2015) 1821–1833.","apa":"Sprechmann, P., Bronstein, A. M., &#38; Sapiro, G. (2015). Learning efficient sparse and low rank models. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE. <a href=\"https://doi.org/10.1109/tpami.2015.2392779\">https://doi.org/10.1109/tpami.2015.2392779</a>","mla":"Sprechmann, P., et al. “Learning Efficient Sparse and Low Rank Models.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 37, no. 9, IEEE, 2015, pp. 1821–33, doi:<a href=\"https://doi.org/10.1109/tpami.2015.2392779\">10.1109/tpami.2015.2392779</a>.","ieee":"P. Sprechmann, A. M. Bronstein, and G. Sapiro, “Learning efficient sparse and low rank models,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 37, no. 9. IEEE, pp. 1821–1833, 2015.","ama":"Sprechmann P, Bronstein AM, Sapiro G. Learning efficient sparse and low rank models. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2015;37(9):1821-1833. doi:<a href=\"https://doi.org/10.1109/tpami.2015.2392779\">10.1109/tpami.2015.2392779</a>"},"quality_controlled":"1","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","status":"public","external_id":{"arxiv":["1212.3631"],"pmid":["26353129"]},"volume":37,"date_created":"2024-10-15T11:20:55Z","_id":"18415","page":"1821-1833","author":[{"last_name":"Sprechmann","first_name":"P.","full_name":"Sprechmann, P."},{"first_name":"Alexander","full_name":"Bronstein, Alexander","orcid":"0000-0001-9699-8730","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein"},{"full_name":"Sapiro, G.","first_name":"G.","last_name":"Sapiro"}],"day":"01","title":"Learning efficient sparse and low rank models","pmid":1,"date_updated":"2024-12-18T11:40:35Z","publication_identifier":{"eissn":["1939-3539"],"issn":["0162-8828"]},"date_published":"2015-09-01T00:00:00Z","month":"09","oa":1,"abstract":[{"text":"Parsimony, including sparsity and low rank, has been shown to successfully model data in numerous machine learning and signal processing tasks. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with parsimony-promoting terms. The inherently sequential structure and data-dependent complexity and latency of iterative optimization constitute a major limitation in many applications requiring real-time performance or involving large-scale data. Another limitation encountered by these modeling techniques is the difficulty of their inclusion in discriminative learning scenarios. In this work, we propose to move the emphasis from the model to the pursuit algorithm, and develop a process-centric view of parsimonious modeling, in which a learned deterministic fixed-complexity pursuit process is used in lieu of iterative optimization. We show a principled way to construct learnable pursuit process architectures for structured sparse and robust low rank models, derived from the iteration of proximal descent algorithms. These architectures learn to approximate the exact parsimonious representation at a fraction of the complexity of the standard optimization methods. We also show that appropriate training regimes allow to naturally extend parsimonious models to discriminative settings. State-of-the-art results are demonstrated on several challenging problems in image and audio processing with several orders of magnitude speed-up compared to the exact optimization algorithms.","lang":"eng"}],"extern":"1","doi":"10.1109/tpami.2015.2392779","publication_status":"published","arxiv":1,"oa_version":"Preprint","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1212.3631","open_access":"1"}],"type":"journal_article"},{"issue":"12","year":"2015","publisher":"IEEE","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"intvolume":"        37","article_processing_charge":"No","scopus_import":"1","external_id":{"pmid":["26539854"]},"citation":{"apa":"Eynard, D., Kovnatsky, A., Bronstein, M. M., Glashoff, K., &#38; Bronstein, A. M. (2015). Multimodal manifold snalysis by simultaneous diagonalization of Laplacians. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE. <a href=\"https://doi.org/10.1109/tpami.2015.2408348\">https://doi.org/10.1109/tpami.2015.2408348</a>","short":"D. Eynard, A. Kovnatsky, M.M. Bronstein, K. Glashoff, A.M. Bronstein, IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (2015) 2505–2517.","ista":"Eynard D, Kovnatsky A, Bronstein MM, Glashoff K, Bronstein AM. 2015. Multimodal manifold snalysis by simultaneous diagonalization of Laplacians. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(12), 2505–2517.","chicago":"Eynard, Davide, Artiom Kovnatsky, Michael M. Bronstein, Klaus Glashoff, and Alex M. Bronstein. “Multimodal Manifold Snalysis by Simultaneous Diagonalization of Laplacians.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE, 2015. <a href=\"https://doi.org/10.1109/tpami.2015.2408348\">https://doi.org/10.1109/tpami.2015.2408348</a>.","ama":"Eynard D, Kovnatsky A, Bronstein MM, Glashoff K, Bronstein AM. Multimodal manifold snalysis by simultaneous diagonalization of Laplacians. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2015;37(12):2505-2517. doi:<a href=\"https://doi.org/10.1109/tpami.2015.2408348\">10.1109/tpami.2015.2408348</a>","mla":"Eynard, Davide, et al. “Multimodal Manifold Snalysis by Simultaneous Diagonalization of Laplacians.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 37, no. 12, IEEE, 2015, pp. 2505–17, doi:<a href=\"https://doi.org/10.1109/tpami.2015.2408348\">10.1109/tpami.2015.2408348</a>.","ieee":"D. Eynard, A. Kovnatsky, M. M. Bronstein, K. Glashoff, and A. M. Bronstein, “Multimodal manifold snalysis by simultaneous diagonalization of Laplacians,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 37, no. 12. IEEE, pp. 2505–2517, 2015."},"quality_controlled":"1","status":"public","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":37,"_id":"18416","author":[{"last_name":"Eynard","first_name":"Davide","full_name":"Eynard, Davide"},{"first_name":"Artiom","full_name":"Kovnatsky, Artiom","last_name":"Kovnatsky"},{"first_name":"Michael M.","full_name":"Bronstein, Michael M.","last_name":"Bronstein"},{"first_name":"Klaus","full_name":"Glashoff, Klaus","last_name":"Glashoff"},{"id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","full_name":"Bronstein, Alexander","first_name":"Alexander","orcid":"0000-0001-9699-8730"}],"page":"2505-2517","date_created":"2024-10-15T11:20:55Z","day":"01","publication_identifier":{"issn":["0162-8828"],"eissn":["1939-3539"]},"date_updated":"2024-12-12T14:08:46Z","date_published":"2015-12-01T00:00:00Z","title":"Multimodal manifold snalysis by simultaneous diagonalization of Laplacians","pmid":1,"month":"12","abstract":[{"lang":"eng","text":"We construct an extension of spectral and diffusion geometry to multiple modalities through simultaneous diagonalization of Laplacian matrices. This naturally extends classical data analysis tools based on spectral geometry, such as diffusion maps and spectral clustering. We provide several synthetic and real examples of manifold learning, object classification, and clustering, showing that the joint spectral geometry better captures the inherent structure of multi-modal data. We also show the relation of many previous approaches for multimodal manifold analysis to our framework."}],"extern":"1","doi":"10.1109/tpami.2015.2408348","publication_status":"published","type":"journal_article","oa_version":"None"},{"abstract":[{"text":"Informative and discriminative feature descriptors play a fundamental role in deformable shape analysis. For example, they have been successfully employed in correspondence, registration, and retrieval tasks. In recent years, significant attention has been devoted to descriptors obtained from the spectral decomposition of the Laplace-Beltrami operator associated with the shape. Notable examples in this family are the heat kernel signature (HKS) and the recently introduced wave kernel signature (WKS). The Laplacian-based descriptors achieve state-of-the-art performance in numerous shape analysis tasks; they are computationally efficient, isometry-invariant by construction, and can gracefully cope with a variety of transformations. In this paper, we formulate a generic family of parametric spectral descriptors. We argue that to be optimized for a specific task, the descriptor should take into account the statistics of the corpus of shapes to which it is applied (the \"signal\") and those of the class of transformations to which it is made insensitive (the \"noise\"). While such statistics are hard to model axiomatically, they can be learned from examples. Following the spirit of the Wiener filter in signal processing, we show a learning scheme for the construction of optimized spectral descriptors and relate it to Mahalanobis metric learning. The superiority of the proposed approach in generating correspondences is demonstrated on synthetic and scanned human figures. We also show that the learned descriptors are robust enough to be learned on synthetic data and transferred successfully to scanned shapes.","lang":"eng"}],"date_updated":"2024-12-12T12:42:56Z","publication_identifier":{"issn":["0162-8828"],"eissn":["1939-3539"]},"date_published":"2014-01-01T00:00:00Z","title":"Learning spectral descriptors for deformable shape correspondence","pmid":1,"month":"01","doi":"10.1109/tpami.2013.148","publication_status":"published","type":"journal_article","oa_version":"None","extern":"1","scopus_import":"1","external_id":{"pmid":["24231874"]},"quality_controlled":"1","citation":{"ama":"Litman R, Bronstein AM. Learning spectral descriptors for deformable shape correspondence. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2014;36(1):171-180. doi:<a href=\"https://doi.org/10.1109/tpami.2013.148\">10.1109/tpami.2013.148</a>","mla":"Litman, R., and Alex M. Bronstein. “Learning Spectral Descriptors for Deformable Shape Correspondence.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 36, no. 1, IEEE, 2014, pp. 171–80, doi:<a href=\"https://doi.org/10.1109/tpami.2013.148\">10.1109/tpami.2013.148</a>.","ieee":"R. Litman and A. M. Bronstein, “Learning spectral descriptors for deformable shape correspondence,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 36, no. 1. IEEE, pp. 171–180, 2014.","apa":"Litman, R., &#38; Bronstein, A. M. (2014). Learning spectral descriptors for deformable shape correspondence. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE. <a href=\"https://doi.org/10.1109/tpami.2013.148\">https://doi.org/10.1109/tpami.2013.148</a>","short":"R. Litman, A.M. Bronstein, IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (2014) 171–180.","chicago":"Litman, R., and Alex M. Bronstein. “Learning Spectral Descriptors for Deformable Shape Correspondence.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE, 2014. <a href=\"https://doi.org/10.1109/tpami.2013.148\">https://doi.org/10.1109/tpami.2013.148</a>.","ista":"Litman R, Bronstein AM. 2014. Learning spectral descriptors for deformable shape correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36(1), 171–180."},"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","status":"public","issue":"1","year":"2014","publisher":"IEEE","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"article_processing_charge":"No","intvolume":"        36","day":"01","volume":36,"_id":"18413","page":"171-180","author":[{"last_name":"Litman","first_name":"R.","full_name":"Litman, R."},{"full_name":"Bronstein, Alexander","first_name":"Alexander","orcid":"0000-0001-9699-8730","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6"}],"date_created":"2024-10-15T11:20:55Z"},{"extern":"1","publication_status":"published","doi":"10.1109/tpami.2013.225","arxiv":1,"oa_version":"Preprint","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1207.1522","open_access":"1"}],"type":"journal_article","title":"Multimodal similarity-preserving hashing","pmid":1,"date_updated":"2024-12-12T13:04:27Z","publication_identifier":{"eissn":["1939-3539"],"issn":["0162-8828"]},"date_published":"2014-04-01T00:00:00Z","month":"04","oa":1,"abstract":[{"text":"We introduce an efficient computational framework for hashing data belonging to multiple modalities into a single representation space where they become mutually comparable. The proposed approach is based on a novel coupled siamese neural network architecture and allows unified treatment of intra- and inter-modality similarity learning. Unlike existing cross-modality similarity learning approaches, our hashing functions are not limited to binarized linear projections and can assume arbitrarily complex forms. We show experimentally that our method significantly outperforms state-of-the-art hashing approaches on multimedia retrieval tasks.","lang":"eng"}],"volume":36,"date_created":"2024-10-15T11:20:55Z","_id":"18414","author":[{"first_name":"Jonathan","full_name":"Masci, Jonathan","last_name":"Masci"},{"last_name":"Bronstein","first_name":"Michael M.","full_name":"Bronstein, Michael M."},{"orcid":"0000-0001-9699-8730","full_name":"Bronstein, Alexander","first_name":"Alexander","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6"},{"first_name":"Jurgen","full_name":"Schmidhuber, Jurgen","last_name":"Schmidhuber"}],"page":"824-830","day":"01","year":"2014","publisher":"IEEE","issue":"4","intvolume":"        36","article_processing_charge":"No","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"scopus_import":"1","quality_controlled":"1","citation":{"mla":"Masci, Jonathan, et al. “Multimodal Similarity-Preserving Hashing.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 36, no. 4, IEEE, 2014, pp. 824–30, doi:<a href=\"https://doi.org/10.1109/tpami.2013.225\">10.1109/tpami.2013.225</a>.","ieee":"J. Masci, M. M. Bronstein, A. M. Bronstein, and J. Schmidhuber, “Multimodal similarity-preserving hashing,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 36, no. 4. IEEE, pp. 824–830, 2014.","ama":"Masci J, Bronstein MM, Bronstein AM, Schmidhuber J. Multimodal similarity-preserving hashing. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2014;36(4):824-830. doi:<a href=\"https://doi.org/10.1109/tpami.2013.225\">10.1109/tpami.2013.225</a>","chicago":"Masci, Jonathan, Michael M. Bronstein, Alex M. Bronstein, and Jurgen Schmidhuber. “Multimodal Similarity-Preserving Hashing.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE, 2014. <a href=\"https://doi.org/10.1109/tpami.2013.225\">https://doi.org/10.1109/tpami.2013.225</a>.","ista":"Masci J, Bronstein MM, Bronstein AM, Schmidhuber J. 2014. Multimodal similarity-preserving hashing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36(4), 824–830.","short":"J. Masci, M.M. Bronstein, A.M. Bronstein, J. Schmidhuber, IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (2014) 824–830.","apa":"Masci, J., Bronstein, M. M., Bronstein, A. M., &#38; Schmidhuber, J. (2014). Multimodal similarity-preserving hashing. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE. <a href=\"https://doi.org/10.1109/tpami.2013.225\">https://doi.org/10.1109/tpami.2013.225</a>"},"status":"public","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","external_id":{"pmid":["26353203"],"arxiv":["1207.1522"]}},{"doi":"10.1109/tpami.2011.103","publication_status":"published","type":"journal_article","oa_version":"None","extern":"1","abstract":[{"lang":"eng","text":"SIFT-like local feature descriptors are ubiquitously employed in computer vision applications such as content-based retrieval, video analysis, copy detection, object recognition, photo tourism, and 3D reconstruction. Feature descriptors can be designed to be invariant to certain classes of photometric and geometric transformations, in particular, affine and intensity scale transformations. However, real transformations that an image can undergo can only be approximately modeled in this way, and thus most descriptors are only approximately invariant in practice. Second, descriptors are usually high dimensional (e.g., SIFT is represented as a 128--dimensional vector). In large-scale retrieval and matching problems, this can pose challenges in storing and retrieving descriptor data. We map the descriptor vectors into the Hamming space in which the Hamming metric is used to compare the resulting representations. This way, we reduce the size of the descriptors by representing them as short binary strings and learn descriptor invariance from examples. We show extensive experimental validation, demonstrating the advantage of the proposed approach."}],"date_published":"2012-01-01T00:00:00Z","article_type":"original","date_updated":"2024-11-12T08:34:48Z","publication_identifier":{"eissn":["2160-9292"],"issn":["0162-8828"]},"pmid":1,"title":"LDAHash: Improved matching with smaller descriptors","month":"01","day":"01","volume":34,"page":"66-78","author":[{"first_name":"C.","full_name":"Strecha, C.","last_name":"Strecha"},{"first_name":"Alexander","full_name":"Bronstein, Alexander","orcid":"0000-0001-9699-8730","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein"},{"last_name":"Bronstein","full_name":"Bronstein, M. M.","first_name":"M. M."},{"last_name":"Fua","first_name":"P.","full_name":"Fua, P."}],"_id":"18412","date_created":"2024-10-15T11:20:54Z","scopus_import":"1","external_id":{"pmid":["21576750 "]},"status":"public","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","citation":{"mla":"Strecha, C., et al. “LDAHash: Improved Matching with Smaller Descriptors.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 34, no. 1, Institute of Electrical and Electronics Engineers, 2012, pp. 66–78, doi:<a href=\"https://doi.org/10.1109/tpami.2011.103\">10.1109/tpami.2011.103</a>.","ieee":"C. Strecha, A. M. Bronstein, M. M. Bronstein, and P. Fua, “LDAHash: Improved matching with smaller descriptors,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 34, no. 1. Institute of Electrical and Electronics Engineers, pp. 66–78, 2012.","ama":"Strecha C, Bronstein AM, Bronstein MM, Fua P. LDAHash: Improved matching with smaller descriptors. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2012;34(1):66-78. doi:<a href=\"https://doi.org/10.1109/tpami.2011.103\">10.1109/tpami.2011.103</a>","chicago":"Strecha, C., Alex M. Bronstein, M. M. Bronstein, and P. Fua. “LDAHash: Improved Matching with Smaller Descriptors.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and Electronics Engineers, 2012. <a href=\"https://doi.org/10.1109/tpami.2011.103\">https://doi.org/10.1109/tpami.2011.103</a>.","ista":"Strecha C, Bronstein AM, Bronstein MM, Fua P. 2012. LDAHash: Improved matching with smaller descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence. 34(1), 66–78.","short":"C. Strecha, A.M. Bronstein, M.M. Bronstein, P. Fua, IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (2012) 66–78.","apa":"Strecha, C., Bronstein, A. M., Bronstein, M. M., &#38; Fua, P. (2012). LDAHash: Improved matching with smaller descriptors. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/tpami.2011.103\">https://doi.org/10.1109/tpami.2011.103</a>"},"quality_controlled":"1","issue":"1","publisher":"Institute of Electrical and Electronics Engineers","year":"2012","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","intvolume":"        34"},{"pmid":1,"title":"Shape recognition with spectral distances","date_published":"2011-05-01T00:00:00Z","article_type":"original","date_updated":"2024-10-22T08:02:31Z","publication_identifier":{"issn":["0162-8828"]},"month":"05","abstract":[{"text":"Recent works have shown the use of diffusion geometry for various pattern recognition applications, including nonrigid shape analysis. In this paper, we introduce spectral shape distance as a general framework for distribution-based shape similarity and show that two recent methods for shape similarity due to Rustamov and Mahmoudi and Sapiro are particular cases thereof.","lang":"eng"}],"extern":"1","publication_status":"published","doi":"10.1109/tpami.2010.210","oa_version":"None","type":"journal_article","year":"2011","publisher":"Institute of Electrical and Electronics Engineers","issue":"5","article_processing_charge":"No","intvolume":"        33","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":"1","status":"public","OA_type":"closed access","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","citation":{"ieee":"M. M. Bronstein and A. M. Bronstein, “Shape recognition with spectral distances,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 33, no. 5. Institute of Electrical and Electronics Engineers, pp. 1065–1071, 2011.","mla":"Bronstein, Michael M., and Alex M. Bronstein. “Shape Recognition with Spectral Distances.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 33, no. 5, Institute of Electrical and Electronics Engineers, 2011, pp. 1065–71, doi:<a href=\"https://doi.org/10.1109/tpami.2010.210\">10.1109/tpami.2010.210</a>.","ama":"Bronstein MM, Bronstein AM. Shape recognition with spectral distances. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2011;33(5):1065-1071. doi:<a href=\"https://doi.org/10.1109/tpami.2010.210\">10.1109/tpami.2010.210</a>","chicago":"Bronstein, Michael M, and Alex M. Bronstein. “Shape Recognition with Spectral Distances.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and Electronics Engineers, 2011. <a href=\"https://doi.org/10.1109/tpami.2010.210\">https://doi.org/10.1109/tpami.2010.210</a>.","ista":"Bronstein MM, Bronstein AM. 2011. Shape recognition with spectral distances. IEEE Transactions on Pattern Analysis and Machine Intelligence. 33(5), 1065–1071.","apa":"Bronstein, M. M., &#38; Bronstein, A. M. (2011). Shape recognition with spectral distances. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/tpami.2010.210\">https://doi.org/10.1109/tpami.2010.210</a>","short":"M.M. Bronstein, A.M. Bronstein, IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (2011) 1065–1071."},"quality_controlled":"1","external_id":{"pmid":["21135442"]},"volume":33,"date_created":"2024-10-15T11:20:54Z","page":"1065-1071","author":[{"first_name":"Michael M","full_name":"Bronstein, Michael M","last_name":"Bronstein"},{"first_name":"Alexander","full_name":"Bronstein, Alexander","orcid":"0000-0001-9699-8730","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6"}],"_id":"18411","day":"01"}]