Object detection using hierarchical graph-based segmentation

Object detection in real images or videos is challenging because the shapes and sizes of objects vary significantly according to their poses, camera viewing direction, and partial occlusion. Previous detection methods employ sliding-window-based schemes that scan windows across an image, requiring many differently shaped windows to capture shape and size variation. In order to solve this problem, we propose an object detection method using hierarchical graph-based segmentation: color-consistent parts are obtained by part-level segmentation and category-consistent regions are found using object-level segmentation. Thus we can avoid scanning a lot of windows across whole images by using part-level segmentation and robustly detect the objects of various shapes and sizes by using object-level segmentation. In addition, we evaluate detection performance using various classifiers with our detection approach.