Study of Content Based Image Retrieval Using Data Mining Techniques

Abstract

The field of image retrieval has been an active research area for several decades and has been paid more and more attention in recent years as a result of the dramatic and fast increase in the volume of digital images. Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and un annotated image databases. In recent years, a variety of techniques have been developed to improve the performance of CBIR. In reaction to the needs of users, who feel problems connected with traditional methods of image searching and indexing, researchers focus their interest on techniques for retrieving images on the basis of automatically-derived features, often denoted as Content-Based Image Retrieval (CBIR). CBIR systems index the media documents using salient features extracted from the actual media rather than by textual annotations. Query by content is nowadays a very active research field, with many systems being developed by industrial and academic teams. Results performed by these teams are really promising. The situation gets diametrically different when we move our attention from the usual CBIR task, i.e. the retrieval of images which are similar (as a whole) to the query image, to the task “find all images that contain the query image”. The proposed CBIR technique uses more than one clustering techniques to improve the performance of CBIR. This optimized method makes use of K-means and Hierarchical clustering technique to improve the execution time and performance of image retrieval systems in high dimensional sets. In this similarity measure is totally based on colors. In this paper more focus area is the way of combination of clustering technique in order to get faster output of images. In this paper the clustering techniques are discussed and analyzed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR. This method makes use of hierarchical and divides and conquers 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.