Image feature fusion and clustering cooperative expression method and system of essential manifold structure

Abstract

The invention discloses an image feature fusion and clustering cooperative expression method and system of an essential manifold structure. The invention includes inputting a group of similar matrices of images in different feature spaces, and using a non-linear fusion operator based on tensor product Extract the sample manifold structure from this group of similarity matrices; further quantify the influence of the noise intensity in the input similarity matrix on the manifold structure through weight learning, and enhance the robustness of the fused manifold structure to noise; comprehensively use the nearest neighbor constraint and Lapp The Lars low-rank constraint ensures the locality of the manifold structure and the consistency with the cluster distribution, and realizes the collaborative expression of feature fusion and clustering. The image feature fusion and clustering cooperative expression method of the essential manifold structure in this embodiment can obtain the detailed information of the corresponding topological structure of the image data in different feature spaces, has strong universality and robustness, and has anti-noise interference, The advantages of high clustering accuracy.

Description

technical field [0001] The invention relates to image feature fusion and clustering technology, in particular to an image feature fusion and clustering cooperative expression method and system of a constrained essential manifold structure. Background technique [0002] In the current era, images have become a major information carrier, playing an increasingly important role in social development and economic construction. Using statistical or machine learning methods to mine different types of features in images makes it possible to observe image characteristics from multiple angles and distinguish samples with different labels in images with high precision. Compared with a single feature, multiple different types of features provide more angles and different levels of sample information. Through mutual support, supplementation, and correction, they can provide essential correlation information between image data. It has been used in hyperspectral remote sensing images. Ana...

AI Technical Summary

Problems solved by technology

At present, this type of method mainly has the following problems: First, the operator uses the following assumptions to mine the relationship between samples, that is, if the similarity between samples a and b, c and d is higher, then samples a and c, b The stronger the correlation between and d

However, this assumption is not always satisfied in practical applications, leading to the diffusion of some wrongly associated information; second, the information diffusion of this operator in different feature spaces is global, which is different from the data in practical applications. is locally correlated with a contradictory condition

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