Web Image Retrieval Using Clustering Approaches
Image retrieval system is an active area to propose a new approach to retrieve images from the large image database. In this concerned, the authors proposed an algorithm to represent images using divisive based and partitioned based clustering approaches. The HSV color component and Haar wavelet transform is used to extract image features. These features are taken to segment an image to obtain objects. For segmenting an image, they used modified k-means clustering algorithm to group similar pixel together into K groups with cluster centers. To modify Kmeans, they proposed a divisive based clustering algorithm to determine the number of cluster and get back with number of cluster to k-means to obtain significant object groups.