2008 | OriginalPaper | Buchkapitel
Learning to Localize Objects with Structured Output Regression
verfasst von : Matthew B. Blaschko, Christoph H. Lampert
Erschienen in: Computer Vision – ECCV 2008
Verlag: Springer Berlin Heidelberg
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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 performance over binary training as well as the best previously published scores.