Tensor Power Iteration for Multi-Graph Matching
Xinchu Shi, Haibin Ling, Weiming Hu, Junliang Xing, Yanning Zhang
Abstract
Due to its wide range of applications, matching between two graphs has been extensively studied and remains an active topic. By contrast, it is still under-exploited on how to jointly match multiple graphs, partly due to its intrinsic computational intractability. In this work, we address this challenging problem in a principled way under the rank-1 tensor approximation framework. In particular, we formulate multi-graph matching as a combinational optimization problem with two main ingredients: unary matching over graph vertices and structure matching over graph edges, both of which across multiple graphs. Then we propose an efficient power iteration solution for the resulted NP-hard optimization problem. The proposed algorithm has several advantages: 1) the intrinsic matching consistency across multiple graphs based on the high-order tensor optimization; 2) the free employment of powerful high-order node affinity; 3) the flexible integration between various types of node affinities and edge/hyper-edge affinities. Experiments on diverse and challenging datasets validate the effectiveness of the proposed approach in comparison with state-of-the-arts.
BibTeX
@inproceedings{cvpr2016_tensorpoweritera,
title = {Tensor Power Iteration for Multi-Graph Matching},
author = {Xinchu Shi and Haibin Ling and Weiming Hu and Junliang Xing and Yanning Zhang},
booktitle = {CVPR 2016},
year = {2016}
}