Integrating kAS and SIFT-like Descriptor for Image Description

Abstract

Shape-descriptor (e.g. Adjacent Contour Segments, i.e. kAS) and key point-descriptor (e.g. Scale Invariant Feature Transform, i.e. SIFT) are widely used for computer vision. However, few works principally integrate shape-descriptor and key point-descriptor to describe the content of an image. On one hand, in some cases the degree of locality of keying-descriptor is too high to capture semantic characteristics of an object. On the other hand, though the shape has higher semantic level than key point, it contains no texture information because only the information of contour/edge is used. To make full use of the information of both shape and key point for generate robust and distinctive features, in this paper we propose an algorithm to integrate shape and key point descriptor. Specifically, we employ kAS to extract useful shape information. Then key points of a kAS shape are defined at which we propose to extract SIFT-like features. Experimental results on image matching demonstrate the effectiveness of the proposed algorithm.