Skeleton-Based 3D Object Retrieval Using Retina-Like Feature Descriptor

Skeleton-based 3D object retrieval is a very efficient method to query the sketch databases in numerous applications. However, few skeleton images are found so far in existing sketch benchmarks. In this paper, we provide an initial benchmark dataset consisting of skeleton sketches, including hand-drawn skeletons and skeletons extracted from 3D objects, and both of them are used to form a generic object class. Then we present a method for skeleton-based 3D object retrieval using a retina-like feature descriptor (S3DOR-RFD) based on the structural property of the human retina for processing complex visual information in a very efficient way. As part of the S3DOR-RFD algorithm, we combine artificial bee colony (ABC) in support vector machine (SVM) so as to improve the performance with automatic parameter selection, where one can make full use of the advantages of ABC and SVM to further improve the accuracy rate of 3D object retrieval. Experimental results indicate that skeleton sketches can be automatically distinguished from perspective sketches, and that the proposed S3DOR-RFD method works efficiently for selected object classes.