representative views re-ranking for 3d model retrieval with multi-bipartite graph reinforcement model
In this paper, we propose a multi-bipartite graph reinforcement model for representative views re-ranking in 3D model retrieval. Given the views of one query 3D model, all query views are grouped into clusters to generate representative views and corresponding original weights. In the retrieval procedure, labeled positive retrieval results are employed to refine the query information. Each group of views from positive retrieval results and the group of representative query views are employed to construct a bipartite graph, and a multi-bipartite graph reinforcement algorithm is performed on these bipartite graphs to re-rank all views. Then the weights of all representative query views are updated. Experimental results on two 3D model databases are provided to justify the effectiveness of the proposed method.
3d model retrieval representative views ranking