Discriminative Semantic Parts Learning for Object Detection

抄録

In this letter, we propose a new semantic parts learning approach to address the object detection problem with only the bounding boxes of object category labels. Our main observation is that even though the appearance and arrangement of object parts might have variations across the instances of different object categories, the constituent parts still maintain geometric consistency. Specifically, we propose a discriminative clustering method with sparse representation refinement to discover the mid-level semantic part set automatically. Then each semantic part detector is learned by the linear SVM in a one-vs-all manner. Finally, we utilize the learned part detectors to score the test image and integrate all the response maps of part detectors to obtain the detection result. The learned class-generic part detectors have the ability to capture the objects across different categories. Experimental results show that the performance of our approach can outperform some recent competing methods.