View-based 3D object retrieval by bipartite graph matching

Bipartite graph matching has been investigated in multiple view matching for 3D object retrieval. However, existing methods employ one-to-one vertex matching scheme while more than two views may share close semantic meanings in practice. In this work, we propose a bipartite graph matching method to measure the distance between two objects based on multiple views. In the proposed method, representative views are first selected by using view clustering for each object, and the corresponding weights are given based on the cluster results. A bipartite graph is constructed by using the two groups of representative views from two compared objects. To calculate the similarity between two objects, the bipartite graph is first partitioned to several subsets, and the views in the same sub-set are with high possibility to be with similar semantic meanings. The distances between two objects within individual subsets are then assembled through the graph to obtain the final similarity. Experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed algorithm