An Efficient Hierarchical Image Segmentation Approach and its Application on Object Localization

To improve the generality and computational efficiency of hierarchical image segmentation method, this paper proposes an efficient hierarchical segmentation approach, and explores its application on object localization. In the g Pb-owt-ucm framework, we improve the watershed transform algorithm using non-maximal suppressed edge, and then construct the full version of hierarchical segmentation with top-performing edge detector. Then, the hierarchical segmentation approach is used for object localization algorithm Selective Search. The diversity strategy of Selective Search is redesigned by changing the edge detection scale and UCM threshold, and an object proposals ranking method based on the classifier is proposed. The experiment on BSDS500 shows that our approach generates the high quality segmentation in a higher speed without the need of edge direction signal. The experiment on Pascal VOC 2007 shows that the improved object localization algorithm can generate more precise object proposals.