Rapid Shape Retrieval Using Improved Graph Transduction

In this paper, we focus on the problem of shape retrieval. A novel approach, called improved graph transduction, is proposed. As preceding graph transduction method, we regard the shape as a node in a graph and the similarity of shapes is represented by the edge of the graph. Then we learn a new distance measure between the query shape and the testing shapes. The main contribution of our work is to merge the most likely node with the query node during the learning process. The appending process helps us to mine the latent information in the propagation. The experimental results on the MPEG-7 data set show that comparing with the existing methods, our method can complete shape retrieval with similar correct rate in less time.