[{"date_created":"2018-12-11T12:04:41Z","month":"09","status":"public","extern":"1","publication":"2008 IEEE Conference on Computer Vision and Pattern Recognition","acknowledgement":"This work was funded in part by the EC project CLASS, IST 027978.","doi":"10.1109/CVPR.2008.4587448","publication_status":"published","date_updated":"2026-06-12T11:40:17Z","oa_version":"None","date_published":"2008-09-18T00:00:00Z","day":"18","publication_identifier":{"issn":["1063-6919"],"isbn":["9781424422425"]},"page":"1 - 8","main_file_link":[{"url":"http://pub.ist.ac.at/~chl/papers/lampert-cvpr2008b.pdf"}],"publist_id":"2657","article_processing_charge":"No","abstract":[{"lang":"eng","text":"We propose a new method to partition an unlabeled dataset, called Discriminative Context Partitioning (DCP). It is motivated by the idea of splitting the dataset based only on how well the resulting parts can be separated from a context class of disjoint data points. This is in contrast to typical clustering techniques like K-means that are based on a generative model by implicitly or explicitly searching for modes in the distribution of samples. The discriminative criterion in DCP avoids the problems that density based methods have when the a priori assumption of multimodality is violated, when the number of samples becomes small in relation to the dimensionality of the feature space, or if the cluster sizes are strongly unbalanced. We formulate DCP&amp;amp;amp;amp;amp;amp;amp;amp;amp;lsquo;s separation property as a large-margin criterion, and show how the resulting optimization problem can be solved efficiently. Experiments on the MNIST and USPS datasets of handwritten digits and on a subset of the Caltech256 dataset show that, given a suitable context, DCP can achieve good results even in situation where density-based clustering techniques fail."}],"title":"Partitioning of image datasets using discriminative context information","publisher":"IEEE","author":[{"full_name":"Lampert, Christoph","first_name":"Christoph","orcid":"0000-0001-8622-7887","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","conference":{"end_date":"2008-06-28","start_date":"2008-06-23","name":"CVPR: Computer Vision and Pattern Recognition","location":"Anchorage, AK, United States"},"_id":"3700","OA_type":"closed access","year":"2008","type":"conference","language":[{"iso":"eng"}],"citation":{"ista":"Lampert C. 2008. Partitioning of image datasets using discriminative context information. 2008 IEEE Conference on Computer Vision and Pattern Recognition. CVPR: Computer Vision and Pattern Recognition, 1–8.","short":"C. Lampert, in:, 2008 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2008, pp. 1–8.","ama":"Lampert C. Partitioning of image datasets using discriminative context information. In: <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>. IEEE; 2008:1-8. doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587448\">10.1109/CVPR.2008.4587448</a>","apa":"Lampert, C. (2008). Partitioning of image datasets using discriminative context information. In <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i> (pp. 1–8). Anchorage, AK, United States: IEEE. <a href=\"https://doi.org/10.1109/CVPR.2008.4587448\">https://doi.org/10.1109/CVPR.2008.4587448</a>","chicago":"Lampert, Christoph. “Partitioning of Image Datasets Using Discriminative Context Information.” In <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>, 1–8. IEEE, 2008. <a href=\"https://doi.org/10.1109/CVPR.2008.4587448\">https://doi.org/10.1109/CVPR.2008.4587448</a>.","mla":"Lampert, Christoph. “Partitioning of Image Datasets Using Discriminative Context Information.” <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>, IEEE, 2008, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587448\">10.1109/CVPR.2008.4587448</a>.","ieee":"C. Lampert, “Partitioning of image datasets using discriminative context information,” in <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>, Anchorage, AK, United States, 2008, pp. 1–8."}},{"author":[{"first_name":"Matthew","full_name":"Blaschko, Matthew","last_name":"Blaschko"},{"last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","conference":{"end_date":"2008-06-28","start_date":"2008-06-23","name":"CVPR: Computer Vision and Pattern Recognition","location":"Anchorage, AK, United States"},"_id":"3712","year":"2008","type":"conference","language":[{"iso":"eng"}],"citation":{"short":"M. Blaschko, C. Lampert, in:, 2008 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2008, pp. 1–8.","ama":"Blaschko M, Lampert C. Correlational spectral clustering. In: <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>. IEEE; 2008:1-8. doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587353\">10.1109/CVPR.2008.4587353</a>","ista":"Blaschko M, Lampert C. 2008. Correlational spectral clustering. 2008 IEEE Conference on Computer Vision and Pattern Recognition. CVPR: Computer Vision and Pattern Recognition, 1–8.","mla":"Blaschko, Matthew, and Christoph Lampert. “Correlational Spectral Clustering.” <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>, IEEE, 2008, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587353\">10.1109/CVPR.2008.4587353</a>.","ieee":"M. Blaschko and C. Lampert, “Correlational spectral clustering,” in <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>, Anchorage, AK, United States, 2008, pp. 1–8.","apa":"Blaschko, M., &#38; Lampert, C. (2008). Correlational spectral clustering. In <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i> (pp. 1–8). Anchorage, AK, United States: IEEE. <a href=\"https://doi.org/10.1109/CVPR.2008.4587353\">https://doi.org/10.1109/CVPR.2008.4587353</a>","chicago":"Blaschko, Matthew, and Christoph Lampert. “Correlational Spectral Clustering.” In <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>, 1–8. IEEE, 2008. <a href=\"https://doi.org/10.1109/CVPR.2008.4587353\">https://doi.org/10.1109/CVPR.2008.4587353</a>."},"date_created":"2018-12-11T12:04:45Z","month":"09","status":"public","publication":"2008 IEEE Conference on Computer Vision and Pattern Recognition","extern":"1","publication_status":"published","doi":"10.1109/CVPR.2008.4587353","date_updated":"2026-06-12T11:32:08Z","oa_version":"None","publication_identifier":{"issn":["1063-6919"],"isbn":["9781424422425"]},"date_published":"2008-09-18T00:00:00Z","day":"18","page":"1 - 8","abstract":[{"lang":"eng","text":"We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common in real world data sources, such as images with text captions. Traditional spectral clustering algorithms either assume that data can be represented by a single similarity measure, or by co-occurrence matrices that are then used in biclustering. In contrast, the proposed method uses separate similarity measures for each data representation, and allows for projection of previously unseen data that are only observed in one representation (e.g. images but not text). We show that this algorithm generalizes traditional spectral clustering algorithms and show consistent empirical improvement over spectral clustering on a variety of datasets of images with associated text."}],"article_processing_charge":"No","publist_id":"2646","title":"Correlational spectral clustering","publisher":"IEEE"},{"author":[{"orcid":"0000-0001-8622-7887","first_name":"Christoph","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert"},{"first_name":"Matthew","full_name":"Blaschko, Matthew","last_name":"Blaschko"},{"last_name":"Hofmann","first_name":"Thomas","full_name":"Hofmann, Thomas"}],"conference":{"location":"Anchorage, AK, United States","end_date":"2008-06-28","name":"CVPR: Computer Vision and Pattern Recognition","start_date":"2008-06-23"},"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","_id":"3714","OA_type":"closed access","year":"2008","type":"conference","language":[{"iso":"eng"}],"citation":{"ista":"Lampert C, Blaschko M, Hofmann T. 2008. Beyond sliding windows: Object localization by efficient subwindow search. 2008 IEEE Conference on Computer Vision and Pattern Recognition. CVPR: Computer Vision and Pattern Recognition, 1–8.","short":"C. Lampert, M. Blaschko, T. Hofmann, in:, 2008 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2008, pp. 1–8.","ama":"Lampert C, Blaschko M, Hofmann T. Beyond sliding windows: Object localization by efficient subwindow search. In: <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>. IEEE; 2008:1-8. doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587586\">10.1109/CVPR.2008.4587586</a>","apa":"Lampert, C., Blaschko, M., &#38; Hofmann, T. (2008). Beyond sliding windows: Object localization by efficient subwindow search. In <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i> (pp. 1–8). Anchorage, AK, United States: IEEE. <a href=\"https://doi.org/10.1109/CVPR.2008.4587586\">https://doi.org/10.1109/CVPR.2008.4587586</a>","chicago":"Lampert, Christoph, Matthew Blaschko, and Thomas Hofmann. “Beyond Sliding Windows: Object Localization by Efficient Subwindow Search.” In <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>, 1–8. IEEE, 2008. <a href=\"https://doi.org/10.1109/CVPR.2008.4587586\">https://doi.org/10.1109/CVPR.2008.4587586</a>.","mla":"Lampert, Christoph, et al. “Beyond Sliding Windows: Object Localization by Efficient Subwindow Search.” <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>, IEEE, 2008, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587586\">10.1109/CVPR.2008.4587586</a>.","ieee":"C. Lampert, M. Blaschko, and T. Hofmann, “Beyond sliding windows: Object localization by efficient subwindow search,” in <i>2008 IEEE Conference on Computer Vision and Pattern Recognition</i>, Anchorage, AK, United States, 2008, pp. 1–8."},"date_created":"2018-12-11T12:04:46Z","month":"09","status":"public","extern":"1","publication":"2008 IEEE Conference on Computer Vision and Pattern Recognition","doi":"10.1109/CVPR.2008.4587586","publication_status":"published","date_updated":"2026-06-12T11:29:05Z","oa_version":"None","page":"1 - 8","day":"18","date_published":"2008-09-18T00:00:00Z","publication_identifier":{"issn":["1063-6919"],"isbn":["9781424422425"]},"main_file_link":[{"url":"http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/pdfs/pdf5070.pdf"}],"article_processing_charge":"No","abstract":[{"text":"Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To perform localization, one can take a sliding window approach, but this strongly increases the computational cost, because the classifier function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branchand- bound scheme that allows efficient maximization of a large class of classifier functions over all possible subimages. It converges to a globally optimal solution typically in sublinear time. We show how our method is applicable to different object detection and retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest neighbor classifiers based on the 2-distance. We demonstrate state-of-the-art performance of the resulting systems on the UIUC Cars dataset, the PASCAL VOC 2006 dataset and in the PASCAL VOC 2007 competition.","lang":"eng"}],"publist_id":"2644","title":"Beyond sliding windows: Object localization by efficient subwindow search","publisher":"IEEE"}]