SURVEY PAPER ON VARIOUS METHODS IN CONTENT BASED INFORMATION RETRIEVAL

Information Retrieval is an emerging research area in the field of Information Retrieval. Due to the immense amount of data in the WWW, it is very tough for the user to retrieve the relevant images. Traditional Image Retrieval approaches based on topic similarity alone is not sufficient nowadays the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. 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 K- Means 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. This survey gives an introduction to content-based image Retrieval and explores the different types of retrieval methods