Image Clustering Using Color Moments and BTC Approach Based on Color Features

Image clustering is an approach to group a set of image data into different meaningful categories.The precision of the image retrieving will be much more improved in the content-based image retrieval if using the low-level visual features to cluster the images efficiently.In this paper,color moment and Block Truncation Coding(BTC) were used to extract color features,and K-Means clustering algorithm was conducted to cluster the image data based on the features.The experiment showed that the method of BTC is better than Color Moment in clustering.