An improved retrieval performance with hybrid shape descriptor and feature matching

Research on Content Based Image Retrieval (CBIR) has become popular as it offers solutions to overcome or complement the drawbacks of Text Based Image Retrieval (TBIR). In CBIR, feature extraction and feature matching are two critical processes, which are of high importance to the retrieval performance of the system. This paper introduces a new approach to shape-based image retrieval by combining global and local shape features using Zernike moments (ZM) and edge-gradient co-occurrence matrix (EGCM) respectively. Two-stage matching strategy is then used to measure similarity between images. Our proposed method achieves higher precision rate compared to other commonly used shape feature.