Segmentation of Color Images using Mean Shift Algorithm for Feature Extraction

The use of low-level visual features to retrieve relevant information from image and video databases has drawn much research attention in recent years. Color is perhaps the most dominant and distinguishing visual feature. In modern CBIR systems, statistical clustering methods are often used to extract visual features, index the feature space, and classify images into semantic categories. Statistical clustering methods are tools that search and generalize concepts based on a large amount of high dimensional numerical data. Feature space analysis is the procedure of recovering the centers of the high-density regions. The technique for representing the significant image features is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. This paper discusses color image segmentation, which is base to feature extraction for content-based image retrieval.