首页|A Self-paced Learning based Transfer Model for Hypergraph Matching
A Self-paced Learning based Transfer Model for Hypergraph Matching
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NSTL
Elsevier
Determination of correspondences between vertexes of two graphs is one of essential tasks in the computer vision fields. Despite the graph matching problem is NP-hard, hypergraph matching is well used in many matching methods from the perspective of higher order geometric information. However, it is still a challenge to learn graph models from observed samples of graph matching. In this paper, we present an effective scheme to parameterize a graph model through self-paced learning algorithm. Consequently, each iteration heuristically selects smaller loss samples in a data-driven manner, the learning and matching problems are aligned to learn a new transfer hypergraph model for constructing its high-order structural attributes for visual object matching. For the final matching task between two graphs, we develop the transfer matching method through the assignment matrix decomposition to achieve it. Several experiments on Willow-Object datasets and some other data sets indicate the good performance of our method. (C) 2022 Published by Elsevier Inc.
NSTL
Elsevier
