title: Learning-based bipartite graph matching for view-based 3D model retrieval.
abstract: Distance measure between two sets of views is one central task in view-based 3D model retrieval. In this paper, we introduce a distance metric learning method for bipartite graph matching-based 3D object retrieval framework. In this method, the relationship among 3D models is formulated by a graph structure with semisupervised learning to estimate the model relevance. More specially, we model two sets of views by using a bipartite graph, on which their optimal matching is estimated. Then, we learn a refined distance metric by using the user’s relevance feedback. The proposed method has been evaluated on four data sets and the experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed method.
authors: Ke
authors: K
authors: Lu
authors: Rongrong
authors: R
authors: Ji
authors: Jinhui
authors: J
authors: Tang
authors: Yue
authors: Y
authors: Gao