Invariant multi-scale shape descriptor for object matching and recognition

Abstract

We propose a novel multi-scale shape descriptor for shape matching and object recognition. The descriptor includes three types of invariants in multiple scales to capture discriminative local and semi-global shape features and the dynamic programming algorithm is employed for shape matching. The experimental results verify that our proposed shape feature is invariant to translation, rotation, scaling, and can well tolerate partial occlusion, articulated variation and intra-class variations. The shape matching and retrieval results on benchmark datasets validate the effectiveness of our method. This method is also applied to real time hand gesture recognition and achieves competitive result compared with state of the arts.