Unsupervised Segmentation of Natural Images Based on Mean Shift and Fuzzy Clustering

Abstract

The invention is an unsupervised segmentation method of natural images based on mean shift and fuzzy clustering, which mainly solves the problem of low accuracy of unsupervised segmentation of massive natural images in the prior art. The scheme is: 1) input an image and smooth it; 2) uniformly initialize 64 iteration initial points in the normalized RGB color space of the smoothed image pixels; 3) perform iterative search on the initial points to obtain 64 converged 4) Delete the convergence points whose number of pixels in the high-dimensional sphere centered on the convergence point is less than the deletion threshold; 5) Merge the convergence points whose Euclidean distance is less than the merger threshold, determine the density peak and the number of density peaks, and calculate the membership degree and pixel 6) Defuzzify the smooth membership of the pixel, add a class label to each pixel, and output the segmented image. The invention does not need to set control parameters, can automatically determine the number of image segmentation categories, and can be used for unsupervised segmentation of massive natural images.

Description

technical field [0001] The technical field of image processing of the present invention is specifically an unsupervised segmentation method for natural images, which can be used in video object tracking and recognition and content-based image retrieval. Background technique [0002] In recent years, with the rapid development of science and technology and computer Internet technology, digital images are used more and more widely in all walks of life. How to quickly identify an image in a large number of images has always been a hot topic of discussion in computer vision and pattern recognition. Since the content-based image retrieval technology was proposed in the early 1990s, it has been a research hotspot for researchers. It mainly extracts the texture, color, shape of the target and their spatial position information of the image, and calculates the information obtained by the image. Retrieve the similarity distance between the image and the image in the data set to real...

AI Technical Summary

Problems solved by technology

For natural images with simple structures, better methods can accurately determine the number of categories, but for images with more complex structures, there will generally be errors

The existing unsupervised segmentation methods of natural images are unstable, which can easily cause over-segmentation or under-segmentation of images

Due to the complexity of natural image scenes, there is no better method to accurately determine the number of categories of natural images

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