Part level transfer regularization for enhancing exemplar SVMs

• EE-SVM, a part based transfer regularization method that boosts E-SVM, is introduced.
• EE-SVM is further improved by transferring the statistics between the parts.
• All the proposed objectives result in convex formulations.
• Experimentally shown that EE-SVM has better performance for pose matched retrieval.
• All objectives are conveniently optimized by mapping to a classical SVM formulation.

Exemplar SVMs (E-SVMs, Malisiewicz et al., ICCV 2011), where an SVM is trained with only a single positive sample, have found applications in the areas of object detection and content-based image retrieval (CBIR), amongst others.In this paper we introduce a method of part based transfer regularization that boosts the performance of E-SVMs, with a negligible additional cost. This enhanced E-SVM (EE-SVM) improves the generalization ability of E-SVMs by softly forcing it to be constructed from existing classifier parts cropped from previously learned classifiers. In CBIR applications, where the aim is to retrieve instances of the same object class in a similar pose, the EE-SVM is able to tolerate increased levels of intra-class variation, including occlusions and truncations, over E-SVM, and thereby increases precision and recall.In addition to transferring parts, we introduce a method for transferring the statistics between the parts and also show that there is an equivalence between transfer regularization and feature augmentation for this problem and others, with the consequence that the new objective function can be optimized using standard libraries.EE-SVM is evaluated both quantitatively and qualitatively on the PASCAL VOC 2007 and ImageNet datasets for pose specific object retrieval. It achieves a significant performance improvement over E-SVMs, with greater suppression of negative detections and increased recall, whilst maintaining the same ease of training and testing.