Enhancing Exemplar SVMs using Part Level Transfer Regularization
published: Oct. 9, 2012, recorded: September 2012, views: 405
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Exemplar SVMs (E-SVMs, Malisiewicz et al, ICCV 2011), where a 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 and deformation over E-SVM, and thereby increases recall.
We make the following contributions: (a) introduce the EE-SVM objective function; (b) demonstrate the improvement in performance of EE-SVM over E-SVM for CBIR; and, (c) 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.
Download slides: bmvc2012_aytar_transfer_regularization_01.pdf (3.5 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !