Image Retrieval Method Based on Sparse Nonnegative Matrix Factorization

Abstract

The invention discloses an image retrieval method based on sparse non-negative matrix decomposition. It includes the following steps: 1) Query and extract the image and accompanying text of the retrieval results under two different image data sources; 2) Extract the tags in the accompanying text, and filter the results according to word frequency to form a vocabulary; 3) For each Image set, using the association relationship between labels and images to form an association matrix between labels and images; 4) Using sparse non-negative matrix decomposition to analyze the association matrix obtained in step 3), to obtain the shared subspaces of different sources of data and their corresponding independent subspaces Subspace; 5) The user sends a retrieval request for images on a data source, forms a query vector and maps it to the corresponding subspace of the data source, calculates the similarity with all images and sorts them, and returns the top N most similar images. The present invention makes full use of the associated knowledge of tags and images under multiple data sources, performs migration learning through sparse non-negative matrix decomposition, and improves the accuracy of image retrieval on target data sources.

Description

technical field [0001] The invention relates to the field of image retrieval, in particular to an image retrieval method based on sparse non-negative matrix decomposition. Background technique [0002] As one of the characteristics of web 2.0, social tags are becoming more and more popular. On websites such as Flickr, YouTube, and Del.icio.us, users can mark photos, videos, web pages, etc., and retrieve related resources according to their interests. However, there are problems such as noise, ambiguity, and subjectivity in the tags added by users, and it is impossible to obtain satisfactory results by directly using the tags marked by users to retrieve resources. Therefore, how to improve the effect of image retrieval based on existing labels is a hot issue in current research. In recent years, many methods have been proposed for this problem. However, these methods have a common limitation, that is, most of them use information from a single data source and ignore the ef...

AI Technical Summary

Problems solved by technology

[0004] However, although the method proposed in [1] considers the control of sparsity in matrix decomposition, this method is limited to the use of a single data source (that is, a single matrix); while the method proposed in [2] uses multiple data sources knowledge to assist image retrieval, but ignores the control of sparsity

Images