Advanced Web Image Retrieval Using Clustering Algorithms

In this paper we propose a novel methodology for Web Image retrieval system that takes an image as the input query and retrieves images based on image content. Content Based Image Retrieval is an approach for retrieving semantically-relevant images from an image store based on algorithmically-derived image features. We propose an algorithm to represent images using divisive and partitioned based clustering approaches. The HSV color component and Haar wavelet transform has been used to extract image features. These features are taken to segment an image to obtain objects. For segmenting an image, modified k-means clustering algorithm was used to group similar pixel together into K groups with cluster centers. To modify K-means, a divisive based clustering algorithm has been proposed to determine the number of cluster and get back with number of cluster to k-means to obtain significant object groups. In addition, the similarity distance measure using threshold value and object uniqueness to quantify the results was also measured.