Image retrieval method

Abstract

The invention discloses an image retrieval method. On the basis of an image library comprising N comparison images, the method comprises the following steps: extracting the texture features of a source image and each image in the image library; calculating the similarity degree of texture features of the source image and each image in the image library, and obtaining a first image set according to a first set threshold; extracting the shape features of boundary of the source image and each image in the first image set; calculating the similarity degree of shape features of boundary of the source image and each image in the first image set, and obtaining a second image set according to a second set threshold; obtaining the color features of the source image and each image in the second image set; calculating the similarity degree of color features of the source image and each image in the second image set, and obtaining a third image set according to a third set threshold; arranging and showing images in the third image set. The method combines the similarity degree of texture features, the shape features of boundary and color features of the images, so that the degree of accuracy of image retrieval is greatly improved, and the precision of retrieved results is ensured.

Description

technical field [0001] The invention relates to the technical field of information retrieval, in particular to an image retrieval method. Background technique [0002] With the development of information technology, the application fields of image retrieval are becoming more and more extensive, and it has become an indispensable technology. The important indicators to measure the quality of image retrieval algorithms are time and space complexity besides accuracy. Since the 1970s and 1980s, image retrieval has become a very active research field, mainly using text-based image retrieval technology and content-based image retrieval technology. [0003] Text-based image retrieval technology (TBIR) follows the traditional text retrieval technology. It is difficult to consider the inherent color, texture, shape and other content characteristics of the image itself. Instead, it uses keywords to describe the image. Retrieve related images in the form of . This technology has the ...

AI Technical Summary

Problems solved by technology

This technology has the following two defects: firstly, due to the continuous expansion of the image database, it takes a lot of time and manpower to manually label each image in the database; Connotation, and the process of manually selecting keywords will contain strong subjectivity, which may cause deviations in image understanding and directly affect the retrieval effect of images

Most of the existing content-based image retrieval systems only use a single image retrieval method or use only one type of image features

Therefore, when the query image contains multiple objects or the background is complex, large retrieval errors will be introduced, making the retrieval results inaccurate

Although there is a method of image retrieval based on feature fusion, the method of feature fusion is single, and the retrieval effect is not ideal

Images