{"publication_identifier":{"eissn":["1939-3539"],"issn":["0162-8828"]},"page":"824-830","author":[{"last_name":"Masci","first_name":"Jonathan","full_name":"Masci, Jonathan"},{"first_name":"Michael M.","full_name":"Bronstein, Michael M.","last_name":"Bronstein"},{"last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander","first_name":"Alexander","orcid":"0000-0001-9699-8730"},{"last_name":"Schmidhuber","full_name":"Schmidhuber, Jurgen","first_name":"Jurgen"}],"scopus_import":"1","date_updated":"2024-12-12T13:04:27Z","date_created":"2024-10-15T11:20:55Z","type":"journal_article","arxiv":1,"issue":"4","article_processing_charge":"No","citation":{"ama":"Masci J, Bronstein MM, Bronstein AM, Schmidhuber J. Multimodal similarity-preserving hashing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2014;36(4):824-830. doi:10.1109/tpami.2013.225","ieee":"J. Masci, M. M. Bronstein, A. M. Bronstein, and J. Schmidhuber, “Multimodal similarity-preserving hashing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 4. IEEE, pp. 824–830, 2014.","short":"J. Masci, M.M. Bronstein, A.M. Bronstein, J. Schmidhuber, IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (2014) 824–830.","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.","chicago":"Masci, Jonathan, Michael M. Bronstein, Alex M. Bronstein, and Jurgen Schmidhuber. “Multimodal Similarity-Preserving Hashing.” IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2014. https://doi.org/10.1109/tpami.2013.225.","apa":"Masci, J., Bronstein, M. M., Bronstein, A. M., & Schmidhuber, J. (2014). Multimodal similarity-preserving hashing. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/tpami.2013.225","mla":"Masci, Jonathan, et al. “Multimodal Similarity-Preserving Hashing.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 4, IEEE, 2014, pp. 824–30, doi:10.1109/tpami.2013.225."},"_id":"18414","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","volume":36,"year":"2014","external_id":{"pmid":["26353203"],"arxiv":["1207.1522"]},"month":"04","oa":1,"extern":"1","doi":"10.1109/tpami.2013.225","abstract":[{"lang":"eng","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."}],"quality_controlled":"1","intvolume":" 36","day":"01","status":"public","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1207.1522","open_access":"1"}],"pmid":1,"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","language":[{"iso":"eng"}],"publication_status":"published","publisher":"IEEE","title":"Multimodal similarity-preserving hashing","date_published":"2014-04-01T00:00:00Z"}