Learning context sensitive similarity measure on pair fusion graph
Cheng Wang, Le He, Yingying Zhu, Wenyu Liu · 2013
In this paper, we present a new approach for shape/image retrieval by efficiently fusing different shape similarities, called Pair-Graph Diffusion. Different from other algorithms which linearly integrate different similarity measures, our algorithm adopts Tensor Product Graph(TPG) to combine two shape similarities by fusing two single-graphs into a multi-graph for fusion process. In such way, we gain more shape information in a higher order, and the multigraph is able to better reveal the intrinsic relation between shapes especially when the two input similarities are very complementary. We perform the experiments on two popular image datasets: MPEG-7 shape dataset and Nistér and Stewénius (N-S) dataset, and achieve state-of-arts retrieval rates: 98.87% on MPEG-7 dataset and 3.69 on N-S dataset. The results demonstrate that the proposed method can effectively fuse two similarities. In addition, Multi-graph Diffusion is a general similarity learning algorithm, and it can be easily applied other tasks for ranking/retrieval.