Nearest neighbor graph potential similarity optimization method based on distance transformation

Abstract

The invention discloses a nearest neighbor graph potential similarity optimization method based on distance transformation. The method comprises the following steps: step 1, constructing a nearest neighbor graph structure and a spectral space thereof; step 2, on the basis of the spectral space, constructing a new similarity distance function expression, namely distance transformation, through function analysis and derivation; step 3, constructing a global nearest neighbor graph and using the global nearest neighbor graph for distance transformation; step 4, constructing a local nearest domainmap based on the consistency punishment information and applying the local nearest domain map to distance transformation; and step 5, respectively constructing a gKNN image and an lKNN image by adopting public data, then optimizing a graph structure by utilizing a proposed distance transformation method, and outputting a final result. According to the method, common problems of potential similarity information hidden in a database are developed by using a gKNN image, and the punishment consensus information is further combined to construct an lKNN image. The robustness and the high performanceof the method are proved, and the superiority of PCI information is also proved.

Description

technical field [0001] The present invention relates to the field of retrieval data. The flow structure contained in the data set is very helpful to improve the performance of visual retrieval results, so a distance transformation-based potential similarity optimization method for nearest neighbor graphs is proposed. Background technique [0002] The large amount of multimedia data currently available poses interesting challenges for retrieval systems. With the development of object descriptors, retrieval performance has been greatly improved in both 2D and 3D domains. However, most of the current research results are still unsatisfactory, and how to improve it is a hot issue. In recent years, a lot of research has been done on the latent structure of datasets to improve retrieval efficiency. The basic idea of ​​this technique is to provide an ideal environment (ie database) for a given query input to perform retrieval. In fact, objects (images / 3D graphics) in databases a...

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However, this method suffers from topological changes controlled

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