Integrate multi-modal cues for category-independent object detection and localization

To detect and localize objects is an indispensable step for many computer vision tasks. Most of the state-of-the-art methods of object detection and localization are category-dependent. These methods can achieve a significant performance. However, they are useless for detecting and localizing objects belonging to an unknown category when applying them to an unknown environment. In this paper, a method is proposed for detecting and localizing generic objects without specifying their categories. The proposed method combines diverse cues, including multi-scale saliency, superpixels straddling, intensity, depth and global information, into a uniform Bayesian framework to obtain accurate detection and localization. By comparison to state-of-the-art methods, our experiments show the promising performance of the proposed method based on the PASCAL VOC 08 dataset and our indoor scene dataset.