Image characteristic extraction method

Abstract

The invention discloses an image characteristic extraction method in the technical field of image processing, which comprises the following steps: extracting position information and color information of all pixel points in an image; performing edge area processing on the image, and acquiring position information and color information of each pixel point in the edge area; extracting color self-correlation characteristics from the edge area of the image I, and acquiring a color self-correlation diagram of the edge area; extracting edge direction self-correlation characteristics from all the areas of the image, and acquiring a global edge direction self-correlation diagram; extracting color self-correlation characteristics from all the areas of the image, and acquiring a global color self-correlation diagram; and performing characteristic pre-fusion on the three self-correlation diagrams, and acquiring image characteristics. The image characteristic extraction method realizes more comprehensive image structure-based content description through the characteristic fusion, reduces the computed amount, improves the performance, and has higher accuracy during image retrieval.

Description

technical field [0001] The present invention relates to a method in the technical field of image processing, in particular to an image feature extraction method. Background technique [0002] With the rapid development of multimedia technology and computer networks, the scale of multimedia databases such as digital images has expanded rapidly. In the face of a large number of images distributed in disorder, the traditional retrieval method based on text keywords can no longer meet the needs of users. In order to find images quickly and accurately, in the early 1990s, content-based image retrieval technology (CBIR: content-based image retrieval) came into being. Different from the search method of querying and matching manually marked keywords in the original system, the content-based retrieval technology automatically extracts the visual content features of each image as its index, such as color, texture, shape, etc. Images are sorted by similarity of visual features and r...

AI Technical Summary

Problems solved by technology

However, when this technology calculates, every pixel in the image is treated equally, and the position information of the pixel is ignored, which leads to a considerable amount of calculation.

[0005] After retrieval, it is found that the article "Image retrieval based on shape similarity by edge orientation autocorrelogram (based on Image retrieval of edge direction autocorrelation map similarity) ", the correlation between the edge pixel in the image and other edge pixels in its neighborhood is used as a shape feature, but this technology contains redundant information in the background , when applied to a general image retrieval system, it may cause misjudgment due to redundant information

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