Image Matching Based on Multi-Sensitivity Optimal Transport
This paper proposes a deep image matching network framework based on multi-sensitivity opti-mal transport to address the problem of matching relationships between key points in different images.It utili-zes VGG16 to extract first-order appearance features from images,and then uses Delaunay triangulation to es-tablish a second-order adjacency relationship graph of key points.On this basis,graph convolution is used to learn the feature embedding of key points,and a multi-sensitivity optimal transport is introduced to measure the structured relationship between key points,in order to enhance the key point alignment ability of the image matching framework.This framework achieves good performance on the Pascal VOC Keypoints dataset.
image matchingdeep map neural networkmulti-sensitivity optimal transport
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