An experimental study of alternative shape-based image retrieval techniques

Besides traditional applications (e.g., CAD/CAM and Trademark registry), new multimedia applications such as structured video, animation, and MPEG-7 standard require the storage and management of well-defined objects. These object databases are then queried and searched for different purposes. A sample query might be “find all the scenes that contain a certain object.” Shape of an object is an important feature for image and multimedia similarity retrievals. Therefore, in this study we focus on shape-based object retrieval and conduct a comparison study on four of such techniques (i.e., Fourier descriptors, grid based, Delaunay triangulation, and our proposed MBC-based methods (e.g., MBC-TPVAS)). We measure the effectiveness of the similarity retrieval of the four different shape representation methods in terms of recall and precision. Our results show that the similarity retrieval accuracy of our method (MBC-TPVAS) is as good as that of the other methods, while it observes the lowest computation cost to generate the shape signatures of the objects. Moreover, it has low storage requirement, and a comparable computation cost to compute the similarity between two shape signatures. In addition, MBC-TPVAS requires no normalization of the objects, and is the only method that has direct support for S-RST query types. In this paper, we also propose a new shape description taxonomy.