Object Category Classification Using Occluding Contours

Occluding contour (OC) plays important roles in many computer vision tasks. The study of using OC for visual inference tasks is however limited, partially due to the lack of robust OC acquisition technologies. In this work, benefit from a novel OC computation system, we propose applying OC information to category classification tasks. Specifically, given an image and its estimated occluding contours, we first compute a distance map with regard to the OCs. This map is then used to filter out distracting information in the image. The results are combined with standard recognition methods, bag-of-visual-words in our experiments, for category classification. In addition to the approach, we also present two OC datasets, which to the best of our knowledge are the first publicly available ones. The proposed method is evaluated on both datasets for category classification tasks. In all experiments, the proposed method significantly improves classification performances by about 10 percent.