Intelligent Clustering Algorithm Based Image Processing Using Data Mining

Abstract

At present, an important research idea of image retrieval is to extract some invariant features of the image, then cluster all the features into a certain number of codewords, and then use these codewords to encode the features of each image, so that each image can be represented by a feature vector describing the codeword, so that image retrieval can be carried out effectively. One of the difficulties of this method is the clustering of large-scale features. This paper analyzes the similarity measure and clustering criteria, and proposes that the maximum minimum distance algorithm can be used to cluster in the image database intelligent retrieval system. At the same time, the specific implementation steps are given. Experimental results show that the algorithm can improve the accuracy and efficiency of image clustering. The research results have a certain reference value for the application of intelligent clustering algorithm in image processing.