Search method for relevance feedback images based on ant colony algorithm and probability hypergraph

Abstract

The invention discloses a search method for relevance feedback images based on an ant colony algorithm and a probability hypergraph, which comprises a training stage and a search stage. The training stage comprises the following steps of extracting low-level features of images, studying a dictionary and performing the high level representation of the images in a library image. The search stage comprises the following steps of extracting low-level features of sample images; performing the high level representation of the sample images; constructing an affinity matrix; initializing or updating a pheromone matrix; labeling positive correlation images and negative collation images for search results of all sample images in the image library; calculating a semantic pheromone matrix; calculating an affinity enhancing probability; enhancing the affinity matrix by using the ant colony algorithm; constructing a hypergraph; and returning the search result and finishing search, or updating the pheromone matrix for the next search. According to the method, an efficient and accurate image search technology is provided for image search and a higher use value is realized.

Description

technical field [0001] The invention belongs to the field of image retrieval, in particular to a high-complexity and high-precision image retrieval method. Background technique [0002] With the rapid development of science and technology, with the rapid popularization and development of multimedia technology, the performance of image acquisition equipment has been continuously improved. The new generation of information resources represented by images has become a strategic energy with the same important position as materials and energy. Due to the large amount of information, rich content, and strong expressiveness of images, the effective organization, analysis, retrieval, and management of massive images has become a core issue in many practical application fields. [0003] In traditional image retrieval, a large number of methods are based on the underlying features of images, such as extracting features such as color, shape, and texture, and then matching similar image...

AI Technical Summary

Problems solved by technology

But image information contains not only pixels, but more importantly, it also includes the subjective experience of human vision, and most of these methods stay in the description and learning of the underlying features of the image, compared to the rich and colorful high-level images that humans can understand and use. Semantics, there is still a large gap, which is the so-called "semantic gap" problem

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