Image retrieval method based on group sparse feature selection

A feature selection and image retrieval technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of limited description ability, unrealistic, information loss, etc., to reduce time complexity, superior performance, improve performance effect

CN103810252AInactive Publication Date: 2014-05-21NANJING UNIV OF INFORMATION SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2014-05-21
Estimated Expiration
Not applicable · inactive patent

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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.
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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...

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Embodiment Construction

[0032] The technical scheme of the present invention is described in detail below in conjunction with accompanying drawing:

[0033] A method for image retrieval based on group sparse feature selection in the present invention includes two parts: feature selection and image retrieval, such as figure 1 shown.

[0034] The feature selection steps include:

[0035] Step 1. Acquisition of image pairs and formation of similarity measure vectors: Selecting image pairs in the image library is an important preparatory work for the algorithm proposed in this paper. Starting from the first image, the selection process of image similarity pairs is shown in Fig. 2(a). Since the time complexity of keyword comparison is very low compared to the comparison of image Euclidean distance, this paper first compares the keywords of the first image in the image database with the keywords of the rest of the images, if there are more than two-thirds of the keywords If they are the same, a similar ...