Selective Feature Combination and Automatic Shape Categorization of 3D Models

It is the key problems in 3D model retrieval to obtain good feature and classify models efficiently. Although many feature extraction methods have been proposed, none is adapted to all models. Moreover, it still relies on manual work to classify models. To solve these problems, firstly, the paper proposes a series of selective combination methods which automatically decide each feature´s appropriate weight. The experiments conduct on PSB show that the combined feature performs much better than the best single feature. Secondly, the paper proposes the iterative clustering process to obtain the shape-based 3D models classification based on the combined feature. Experiment shows that the method can classify 91% models of Princeton Shape Benchmark.