Template matching via bipartite graph and graph attention mechanism. Issue 5 (30th December 2022)

• Record Type: Journal Article
• Authors: Zhao, Kai; He, Binbing; Lei, Wei; Zhu, Yuan
• Is Part Of: IET image processing. Volume 17:Issue 5(2023)
• Page Start: 1346
• Page End: 1354
• Publication Date: 2022-12-30
• Subjects: computer vision -- learning (artificial intelligence) -- image matching; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12716
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: Most existing template matching algorithms are global matching between the template target and the search region, which makes the matching process retain a lot of unfavourable background information and ignore the structure and local information of the template target. To address this problem, a template matching algorithm based on bipartite graph and graph attention mechanism is proposed in this paper. The algorithm models the similarity matching problem between template features and search region features as a complete bipartite graph, realises local‐to‐local information transfer between the two, and uses the graph attention mechanism to apply weights between local information to obtain a learnable embedding network module. In addition, in terms of feature representation, a multi‐level feature fusion module based on CNN is introduced, which improves the representation of a target by fusing features with different representational meanings of the target. Experimental results on several typical datasets show that the proposed algorithm achieves leading performance in terms of accuracy and efficiency compared to the two state‐of‐the‐art CNN‐based template matching algorithms, Deep‐DIM and QATM. Abstract : We proposed a template matching algorithm based on bipartite graph and graph attention mechanism. The algorithm achieves local‐to‐local information transfer by modeling the similarity matching problem between template features and search area features as a complete bipartite graph, and uses the graph attention mechanism to apply weights between local information to generate a learnable embedding network module. According to the experimental results, the proposed algorithm achieves leading performance in both accuracy and efficiency.