A Generic Frame Work for Image Data Clustering Via Weighted Clustering Ensemble

This paper has a further exploration and study of visual feature extraction. Image retrieval based on multi-feature fusion is achieved by using normalized Euclidean distance classifier. According to the HSV (Hue, Saturation, Value) color space, the work of color feature extraction is finished, the process is as follows: quantifying the color space in non-equal intervals, constructing one dimension feature vector and representing the color feature by cumulative histogram. Similarly, the work of texture feature extraction is obtained by using gray-level co-occurrence matrix (GLCM) or color cooccurrence matrix (CCM).using color feature ,and texture feature based algorithms clustering temporal data process is very complex. Combining the low-level visual features and highlevel concepts, the proposed approach fully explores the similarities among images in database, using such clustering algorithm and optimizes the relevance results from traditional image retrieval system by firstly clustering the similar images in the images database to improve the efficiency of images retrieval system ,to reduce the complexity to cluster image data ,we propose a dynamic algorithm, weighted ensemble learning approach to image data clustering which combines image data .