Object recognition and segmentation using SIFT and Graph Cuts

Abstract

In this paper, we propose a method of object recognition and segmentation using scale-invariant feature transform (SIFT) and graph cuts. SIFT feature is invariant for rotations, scale changes, and illumination changes and it is often used for object recognition. However, in previous object recognition work using SIFT, the object region is simply presumed by the affine-transformation and the accurate object region was not segmented. On the other hand, graph cuts is proposed as a segmentation method of a detail object region. But it was necessary to give seeds manually. By combing SIFT and graph cuts, in our method, the existence of objects is recognized first by vote processing of SIFT keypoints. After that, the object region is cut out by graph cuts using SIFT keypoints as seeds. Thanks to this combination, both recognition and segmentation are performed automatically under cluttered backgrounds including occlusion.