Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
Content-based retrieval of 3D models
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A new 3D model retrieval approach based on the elevation descriptor
Pattern Recognition
3D model comparison using spatial structure circular descriptor
Pattern Recognition
View-based 3D model retrieval with probabilistic graph model
Neurocomputing
3D model retrieval using weighted bipartite graph matching
Image Communication
A Bayesian 3-D Search Engine Using Adaptive Views Clustering
IEEE Transactions on Multimedia
Less is More: Efficient 3-D Object Retrieval With Query View Selection
IEEE Transactions on Multimedia
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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.