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

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