Image segmentation algorithm based on multi-granularity clustering

Abstract

This article proposes an image processing algorithm based on multi-scale clustering, which can improve the probability of a classifier correctly classifying an image even after it has been subjected to noise attacks. The algorithm mainly involves performing multi-scale clustering on the pixels of the image based on features such as color space, distance, texture, etc., and then incorporating the clustering results into the training of the classifier. By directly extracting the main feature information from the image for training, the algorithm prevents large classification errors due to changes in certain pixel values, thus reducing the impact of attacks on other pixels in the image and greatly improving the robustness of the image. Therefore, this algorithm is a robust and efficient denoising method.