Manifold multi-view image clustering method and system based on adaptive composition

Abstract

The invention discloses a manifold multi-view image clustering method and system based on adaptive composition, and the method comprises the steps: firstly extracting a plurality of features of a same sample image in a sample set, and obtaining a plurality of feature views; then, by utilizing the geodesic distances on all the views, in a combination with a self-adaptive composition method, enabling all the views to share the same clustering indication matrix, and obtaining a manifold similarity matrix on each view through optimization; on the basis of the manifold similarity matrixes of all the views, constructing a regularization optimization objective function, fusing the regularization optimization objective function and the views into a center manifold similarity matrix of the multiple views in a self-adaptive mode, and meanwhile obtaining a multi-view clustering indication matrix corresponding to the center matrix; and finally, clustering the final clustering indication matrix to obtain an image clustering result based on the multi-view features. According to the method and the system, the manifold similarity relation of each image feature view can be effectively expressed, the construction process is supervised through the shared indication matrix, and the accuracy of image clustering can be improved.

Description

technical field [0001] The invention belongs to the field of image recognition, relates to multi-view clustering of image data sets, in particular to an image clustering method and system based on adaptive composition manifold multi-view. Background technique [0002] Image clustering based on multi-view can effectively use different information of each view to improve the accuracy of image clustering. The core difficulty of multi-view clustering lies in how to construct similarity matrix and how to fuse similarity matrix. Today's multi-view clustering methods can be divided into: collaborative training methods, multi-core learning methods, multi-view subspace clustering methods, multi-view adaptive composition clustering methods and multi-task multi-view clustering methods, among which multi-view Subspace clustering and multi-view adaptive composition clustering are the most widely used. Multi-view subspace clustering can be divided into: traditional subspace, low-rank re...

AI Technical Summary

Problems solved by technology

[0003] Before the present invention was made, most of the existing multi-view clustering directly estimated the similarity matrix of each view based on the Euclidean distance, without fully considering the manifold structure of each view

In addition, the existing adaptive methods generally construct the similarity graph matrix of each view independently, and do not make full use of the consistency of the clustering indicator matrix to guide it, so the mutual relationship between multiple views is insufficiently explored

Images