Image retrieval method based on group sparse feature selection

Abstract

The invention discloses an image retrieval method based on group sparse feature selection. The image retrieval method includes a feature selection step and an image retrieval step. The feature selection step includes selecting image pairs, extracting multiple features, forming a characteristic difference matrix, establishing a group sparse logic regression model, utilizing an optimization algorithm to solve weight and selecting the optimum features. The image retrieval step includes extracting optimum features of all images in an image bank, forming an image feature bank, extracting and searching for image optimum features, carrying out similarity comparison, solving the largest similarity image sequence number and outputting retrieval images. According to the image retrieval method based on group sparse feature selection, a self-adaptation spectrum gradient algorithm is adopted to effectively solve the group sparse logic regression model, the rate of convergence is higher and the operating time is shorter. For a traditional image retrieval method based on contents, selection of the features is specified. For the image retrieval method based on group sparse feature selection, image retrieval and comparison are carried out by utilizing all training features, so under the same experiment condition, the image retrieval method based on group sparse feature selection has the advantages that the precision ratio of the images is improved obviously, and the efficiency of image retrieval is improved.

Description

technical field [0001] The invention relates to the technical field of image information processing, in particular to an image retrieval method based on group sparse feature selection. Background technique [0002] With the rapid development of database technology, multimedia technology, and network technology, people are more and more exposed to databases with a large number of digital images. In order to manage the image database effectively, people urgently need an efficient image retrieval system. Due to technical reasons, many popular commercial Web image search engines such as Google, Baidu, 360 Search, etc. are traditional text-based image retrieval. Text-based image retrieval uses the text information associated with images in web pages to complete the search task, but it has the following disadvantages: 1. The description ability is limited, such as texture, irregular shape, etc. cannot be accurately described. 2. The description is subjective, and different people...

AI Technical Summary

Problems solved by technology

Text-based image retrieval uses the text information associated with images in web pages to complete the search task, but it has the following disadvantages: 1. The description ability is limited, such as texture, irregular shape, etc. cannot be accurately described

2. The description is subjective, and different people may describe the same image differently

3. In the face of massive images in modern society, it is obviously unrealistic for each image to have a detailed text description

For the dimension reduction method, there are two problems: 1. The reduced dimension is not blind, because if the dimension is reduced below the necessary dimension, the information of the image features may be lost

2. And because different features belonging to the same kind of features at the bottom layer have strong correlations, dimensionality reduction cannot completely solve the problem of data redundancy

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