Inter-image content constraint-based ultra-pixel coding image retrieval method

Abstract

The invention belongs to the technical field of image processing, and particularly discloses an inter-image content constraint-based ultra-pixel coding image retrieval method, which comprises the following steps of 1) performing ultra-pixel segmentation on images, and recording the position information of each ultra-pixel block; 2) extracting SIFT (scale invariant feature transform) and LBP (length between perpendiculars) fusion features of the ultra-pixel blocks of the images; 3) randomly selecting the fusion features of the ultra-pixel blocks of a training image from an image library, and generating a dictionary in a K-means clustering way; 4) inputting an image to be retrieved, coding the image to be retrieved by virtue of the dictionary, and initially coding the images except the image to be retrieved; 5) initially coding the images in the image libraries, screening the most similar coded value from an area corresponding to the image to be retrieved as a predictive code, and performing constrained coding on the images in the image library; 6) coding the image to be retrieved and the other images in the image library, calculating similarity matching, and displaying a retrieval result according to a matching value. The method is higher in accuracy and callback rate.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a superpixel coded image retrieval method based on content constraints between images, which can be applied to human-computer interaction, information retrieval, and the like. Background technique [0002] Image retrieval has always been one of the key technologies that have attracted much attention in the field of computer vision, and it is also a research hotspot in the academic circles and major Internet companies. Although there are a large number of algorithms to achieve image retrieval, but because people do not have a unified standard for image annotation and understanding, with the improvement of the accuracy of people's information needs, the efficiency and accuracy of image retrieval also make image retrieval more difficult. development is difficult to meet people's needs. [0003] In recent years, superpixel segmentation has gradually become a pop...

AI Technical Summary

Problems solved by technology

It is difficult to achieve better retrieval results when the color of the image changes greatly

[0006] The above two methods are to extract feature codes from the image to be retrieved and the image in the image library, and fail to use the information of the image to be retrieved and the image in the image library. When the color characteristics and spatial position of the image change greatly, it is difficult to achieve efficient retrieval.

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