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Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and unannotated image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So nowadays the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. In recent years, a variety of techniques have been developed to improve the performance of CBIR. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. With large data sets, there is possibility of high dimensionality. Having both accuracy and efficiency for high dimensional data sets with enormous number of samples is a challenging arena. In this paper the clustering techniques are discussed and analysed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR.This method makes use of hierachical and divide and conquer KMeans clustering technique with equivalency and compatible relation concepts to improve the performance of the K-Means for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.

A Survey On: Content Based Image Retrieval Systems Using Clustering Techniques For Large Data sets