Regularizing max-margin exemplars by reconstruction and generative models

Abstract

Part-based models are one of the leading paradigms in visual recognition. In the absence of costly part annotations, associating and aligning different training instances of a part classifier and finding characteristic negatives is challenging and computationally demanding. To avoid this costly mining of training samples, we estimate separate generative models for negatives and positives and integrate them into a max-margin exemplar-based model. The generative model and a sparsity constraint on the correlation between spatially neighboring feature dimensions regularize the part filters during learning and improve their generalization to similar instances. To suppress inappropriate positive part samples, we project the classifier back into the image domain and penalize against deviations from the original exemplar image patch. The part filter is then optimized to i) discriminate against clutter, to ii) generalize to similar instances of the part, and iii) to yield a good reconstruction of the original image patch. Moreover, we propose an approximation for estimating the geometric margin so that learning large numbers of parts becomes feasible. Experiments show improved part localization, object recognition, and part-based reconstruction performance compared to popular exemplar-based approaches on PASCAL VOC.