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

A non-negative matrix decomposition and image clustering technology, which is applied in the field of image processing, can solve the problems of slow clustering speed and low image clustering accuracy, and achieve the effects of improving accuracy, speeding up image clustering speed, and enhancing exclusivity

CN107341510AActive Publication Date: 2017-11-10XIDIAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2017-11-10

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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.
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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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Embodiment Construction

[0042] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0043] refer to figure 1 .The image clustering method based on sparse orthogonal dual-image non-negative matrix decomposition, comprising the following steps:

[0044] Step 1) Input the image data of the image to be clustered:

[0045] The image data set PIE contains 2856 images, with 68 people, and each person has 42 face images with 4 expressions under different lighting and lighting conditions. Each image contains 32×32 pixels / dimension. The image data input in this embodiment is 1050 images randomly selected from the image data set PIE, there are 25 types, each type has 42 different images, and 25 images such as image 3 (a) shown.

[0046] Step 2) Calculate the data space similarity matrix and feature space similarity matrix:

[0047] (2a) Calculate the Euclidean distance O between the data in the data space respectively S ...