Object localization by density-based spatial clustering

Region search is widely used for object localization in computer vision area. After projecting the score of an image classifier into an image plane, region search aims to find regions that precisely localize desired objects. The popular region search methods, such as efficient subwindow search and efficient region search, usually find regions with maximal score. In this paper, we observe that there is a large score density around a desired object usually. Based on this observation, we proposed a region search method by density-based spatial clustering. The resulted regions of this method can guarantee that their density is above a threshold. Besides, this method has linear time complexity for regularly sampling feature points, which is useful for popular bag-of-words feature representation. We demonstrate its superiority for synthetic data and real image dataset for weakly-supervised localization task.