A Graph Matching Method with Windowed Dynamic Space Regularization

A graph matching and dynamic technology, applied in the fields of computer vision and pattern recognition, can solve problems such as difficulty in obtaining the optimal objective function, and achieve the effect of low memory consumption and high matching accuracy

Inactive Publication Date: 2018-10-16
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, the main disadvantages of these two types of algorithms are: the former is an NP-complete problem, and it needs to rely on non-convex optimization to find the result when solving it. It is difficult to obtain the optimal objective function and can only achieve exact graph matching.
The latter can handle non-exact matching problems, but it takes O(n 4 ) storage space, so it can only be adapted to the graph matching problem with a small number of nodes

Method used

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  • A Graph Matching Method with Windowed Dynamic Space Regularization
  • A Graph Matching Method with Windowed Dynamic Space Regularization
  • A Graph Matching Method with Windowed Dynamic Space Regularization

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Embodiment

[0031] figure 1 It is a flowchart of the graph matching method with windowed dynamic space regularization of the present invention.

[0032] Select the face picture in the Willow-Object database for experiment in the present embodiment, as figure 1 As shown, the present invention is based on the graph matching method of band window dynamic space regularization, comprises the following steps:

[0033] 1. Extract the image features of the reference picture image and the image to be matched respectively, and the features may include: single point features, edge features or block features, etc. Create a reference graph G 1 and the graph G to be matched 2 , where G 1 have N 1 nodes G 2 have N 2 nodes Establish reference graph G according to features 1 and the graph G to be matched 2 The ε-adjacency graph of points, such as figure 2 shown. Pre-set threshold ε, calculate the Euclidean distance d between every two feature points in the graph; when the distance is less ...

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Abstract

The invention discloses a graph matching method with dynamic space regularization with a window, which belongs to the field of computer vision and pattern recognition, and more specifically relates to a graph matching method with dynamic space regularization with a window. According to the local feature description of the reference image and the image to be matched, the matching degree between nodes is measured by dynamic space regularization with a window, and the graph matching method is realized, which has the characteristics of less memory consumption and non-exact matching. Aiming at the ε-adjacency graph relationship between the node and its adjacent nodes in the reference image G1, determine the image G2 to establish the subgraph to be matched according to the number of nodes; use the cosine of the included angle in the subgraph to represent the local information of the central node, and use the windowed dynamic Space regularization is used to measure the degree of geometric matching between subgraphs and subgraphs, and the degree of matching support for the central node is given; then the node pair with the highest degree of matching support is selected to complete the graph matching process.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, and more specifically relates to a graph matching method with window dynamic space regularization. Background technique [0002] Finding the mapping relationship between points and points from two point sets has always been the core issue in the field of computer vision and pattern recognition, that is, feature matching, and its applications are found in target recognition, retrieval, positioning, 3D reconstruction, motion segmentation, image deformation, etc. . In recent years, the research on feature matching using graph matching has developed rapidly. The graph can be constructed by extracting two-dimensional and three-dimensional feature points of the target, and then adding weighted edges. This is mainly because: graph matching methods can not only consider the first-order appearance information of features, but also use higher-order, such as side information, for fea...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00
Inventor 郑亚莉程洪潘力立徐立凯
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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