Learning Discriminative Collections of Part Detectors for Object Recognition

Abstract

We propose a method to learn a diverse collection of discriminative parts from object bounding box annotations. Part detectors can be trained and applied individually, which simplifies learning and extension to new features or categories. We apply the parts to object category detection, pooling part detections within bottom-up proposed regions and using a boosted classifier with proposed sigmoid weak learners for scoring. On PASCAL VOC2010, we evaluate the part detectors' ability to discriminate and localize annotated keypoints and their effectiveness in detecting object categories.