Diversified image marking and retrieving method based on radial basis function neural network

Abstract

The invention discloses a diversified image marking and retrieving method based on a radial basis function neural network (RBFNN). The diversified image marking and retrieving method comprises the steps of (1) constructing and learning an RBFNN model capable of covering an image sub-concept; (2) inputting data preprocessed by a retrieval database into the RBFNN model constructed in the step (1), carrying out the diversification marking on images in an image library, and meanwhile marking the images in the image library with labels of concepts and sub-concepts; (3) carrying out the diversification retrieval on the marked image library according to retrieval key words and the marked results of the step (2): firstly searching the images marked with the retrieval key words, and sequencing the images according to the similarity of the concepts, and then bringing the images belonging to the different sub-concepts in the front according to the similarity of the concepts; and (4) outputting the retrieval results. The diversified image marking and retrieving method has the advantages that the image retrieving precision is improved, and meanwhile, the diversity of the image retrieval results is greatly enhanced, the retrieval time is saved, and robustness and practical applicability are high.

Description

technical field [0001] The invention relates to a diversified image labeling and retrieval system, in particular to a diversified image labeling and retrieval method based on RBFNN (radial basis function neural network). Background technique [0002] Image retrieval is a science and technology that emerged and developed with the rapid development of computer science and technology. It has very important application prospects in the fields of national defense, social security, remote sensing, medicine, and business information. In recent years, with the rapid development and popularization of digital equipment such as scanners, digital cameras, and digital video cameras, as well as the improvement of multimedia technology and the rapid popularization of the Internet, image data has grown geometrically, so large-capacity Faced with the increasingly huge information ocean, how to effectively organize, manage and retrieve large-scale image data has become an urgent problem to be...

AI Technical Summary

Problems solved by technology

[0008] The technical problem to be solved by the present invention is to propose a RBFNN-based diversified image labeling and retrieval method for the deficiencies of the existing diversified image retrieval technology

This method not only solves the problem that traditional correlation and diversity cannot be learned in parallel, but also solves the problem of unbalanced data learning, which improves the accuracy and efficiency of image diversity labeling and retrieval

Images