--- res: bibo_abstract: - 'Sliding window classifiers are among the most successful and widely applied techniques for object localization. However, training is typically done in a way that is not specific to the localization task. First a binary classifier is trained using a sample of positive and negative examples, and this classifier is subsequently applied to multiple regions within test images. We propose instead to treat object localization in a principled way by posing it as a problem of predicting structured data: we model the problem not as binary classification, but as the prediction of the bounding box of objects located in images. The use of a joint-kernel framework allows us to formulate the training procedure as a generalization of an SVM, which can be solved efficiently. We further improve computational efficiency by using a branch-and-bound strategy for localization during both training and testing. Experimental evaluation on the PASCAL VOC and TU Darmstadt datasets show that the structured training procedure improves pe rformance over binary training as well as the best previously published scores.@eng' bibo_authorlist: - foaf_Person: foaf_givenName: Matthew foaf_name: Blaschko,Matthew B foaf_surname: Blaschko - foaf_Person: foaf_givenName: Christoph foaf_name: Christoph Lampert foaf_surname: Lampert foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0001-8622-7887 bibo_doi: 10.1007/978-3-540-88682-2_2 bibo_volume: 5302 dct_date: 2008^xs_gYear dct_publisher: Springer@ dct_title: Learning to localize objects with structured output regression@ ...