Image clustering method based on sparse orthogonal bigraph non-negative matrix factorization

Abstract

The invention proposes an image clustering method based on sparse orthogonal bigraph non-negative matrix factorization used for solving the technical problems of low accuracy and slow speed of image clustering in the existing method. The implementation steps are as follows: inputting image data; calculating a data space similarity matrix and a feature space similarity matrix; calculating a data space similarity diagonal matrix and a feature space similarity diagonal matrix; acquiring a label constraint matrix; defining and initializing three sparse orthogonal bigraph non-negative matrix factorization matrixes; setting the number of iterations; acquiring an updating formula of the three sparse orthogonal bigraph non-negative matrix factorization matrixes and an updating formula of the label constraint matrix; defining an updating formula of a coefficient diagonal matrix; updating the three sparse orthogonal bigraph non-negative matrix factorization matrixes, the label constraint matrix and the coefficient diagonal matrix; defining and calculating a low-dimensional data representation matrix; and performing image clustering and output. The image clustering method based on the sparse orthogonal bigraph non-negative matrix factorization provided by the invention can be used for texts, image clustering and face recognition and other practical applications.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image clustering method, in particular to an image clustering method based on sparse orthogonal dual-image non-negative matrix decomposition, which can be used for text clustering, image clustering and face recognition and other practical applications. Background technique [0002] With the development of shooting tools such as cameras, video cameras and smart phones, images have become indispensable data information in people's lives, and image clustering methods, as an effective data organization method, have therefore become one of the hottest research directions. Image clustering is to divide the image data into several categories according to the number of selected categories, so that the intra-class similarity in the same class is as large as possible, while the inter-class similarity between different classes is as small as possible. Commonly used image clusteri...

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Problems solved by technology

[0006] The purpose of the present invention is to solve the problems existing in the above-mentioned existing methods, and propose an image clustering method based on sparse orthogonal dual-image non-negative matrix decomposition, which is used to solve the problems of low image clustering accuracy and The technical problem of slow clustering

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