Content-based image retrieval method based on significance segmentation

Abstract

The invention discloses a content-based image retrieval method based on significance segmentation. The method comprises a training process and a testing process. The training process comprises the following steps that firstly, a visual vocabulary dictionary is established, specifically, all images in a training image set are subjected to analysis and processing sequentially, and the visual vocabulary dictionary is established for subsequent retrieval; secondly, target foreground images and background regional images are obtained through segmentation by means of visual significance features of the images; thirdly, color features and textural features are extracted from the target foreground images and the background regional images; and fourthly, statistics of visual vocabulary distribution histograms of all the images in the database are made on the basis of operation in the first step. The testing process comprises the fifth step that on the basis of operation in the four steps, tested images are retrieved. By adoption of the content-based image retrieval method based on significance segmentation, the number of semantic gaps can be effectively decreased, and the accuracy is high.

Description

technical field [0001] The invention relates to an image retrieval method, in particular to an image retrieval method based on visual salience segmentation, and belongs to the field of content-based image retrieval. Background technique [0002] With the development of multimedia technology, the number of digital images has shown a geometric growth. How to quickly and accurately find the resource requested by the user in the vast image resources is also in front of people. [0003] Content-based image retrieval technology allows users to input a picture to find other pictures with the same or similar content, which represents the mainstream development trend of image retrieval technology. [0004] Josef Sivic et al. proposed the bag-of-words model in 2006. Its core idea is to detect some key points in the whole image, and then extract the local features of these key points, and then these local features are quantified as "visual words". In this way, each image can be repr...

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the shortcomings of existing image retrieval methods, such as semantic gap and poor accuracy, the present invention provides a content-based image retrieval method based on saliency segmentation that effectively reduces the semantic gap and has high accuracy.

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