Abstract
Graph matching is an essential problem in computer vision and pattern recognition. In this paper, we propose a novel graph matching method based on non-negative sparse model (NSGM). The main feature for our NSGM is that it can generate sparse solution and thus naturally imposes the discrete mapping constraints approximately in the optimization process. In addition, an efficient algorithm was derived to solve NSGM problem. Promising experimental results on both synthetic and real image matching tasks show the effectiveness of the proposed matching method.
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Jiang, B., Tang, J., Luo, B. (2013). Graph Matching with Nonnegative Sparse Model. In: Kropatsch, W.G., Artner, N.M., Haxhimusa, Y., Jiang, X. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2013. Lecture Notes in Computer Science, vol 7877. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38221-5_5
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DOI: https://doi.org/10.1007/978-3-642-38221-5_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38220-8
Online ISBN: 978-3-642-38221-5
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