PairingNet: A Learning-based Pair-searching and -matching Network for Image Fragments

• URL: http://arxiv.org/abs/2312.08704v1
• Date: Thu, 14 Dec 2023 07:43:53 GMT
• Title: PairingNet: A Learning-based Pair-searching and -matching Network for Image Fragments
• Authors: Rixin Zhou, Ding Xia, Yi Zhang, Honglin Pang, Xi Yang, Chuntao Li
• Abstract summary: We propose a learning-based image fragment pair-searching and -matching approach to solve the challenging restoration problem. Our proposed network achieves excellent pair-searching accuracy, reduces matching errors, and significantly reduces computational time.
• Score: 6.694162736590122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a learning-based image fragment pair-searching and -matching approach to solve the challenging restoration problem. Existing works use rule-based methods to match similar contour shapes or textures, which are always difficult to tune hyperparameters for extensive data and computationally time-consuming. Therefore, we propose a neural network that can effectively utilize neighbor textures with contour shape information to fundamentally improve performance. First, we employ a graph-based network to extract the local contour and texture features of fragments. Then, for the pair-searching task, we adopt a linear transformer-based module to integrate these local features and use contrastive loss to encode the global features of each fragment. For the pair-matching task, we design a weighted fusion module to dynamically fuse extracted local contour and texture features, and formulate a similarity matrix for each pair of fragments to calculate the matching score and infer the adjacent segment of contours. To faithfully evaluate our proposed network, we created a new image fragment dataset through an algorithm we designed that tears complete images into irregular fragments. The experimental results show that our proposed network achieves excellent pair-searching accuracy, reduces matching errors, and significantly reduces computational time. Details, sourcecode, and data are available in our supplementary material.

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