An Experimental Study on Content Based Image Retrieval Based On Number of Clusters Using Hierarchical Clustering Algorithm

Nowadays the content based image retrieval (CBIR) is becoming a source of exact and fast retrieval. CBIR presents challenges in indexing, accessing of image data and how end systems are evaluated. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. Many clustering and segmentation algorithms both suffer from the limitation of the number of clusters specified by a human user. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters (NC) to return. This paper discusses the image retrieval based on NC which is evaluated using hierarchical agglomerative clustering algorithm (HAC). In this paper, we determine the optimal number of clusters using HAC applied on RGB images and validate them using some validity indices. Based on number of clusters, we retrieve set of images. These cluster values can be further used for divide and conquer technology and indexing for large image dataset. An experimental study is presented on real data sets. Key terms: CBIR, number of clusters, hierarchical agglomerative clustering, validity indices, RGB image