Digital Image Processing

M. S. V. Harish ¹, A. Harshavardhan ², E. R. Suhasini ²

Affiliations
1 Department of Electronics and Communication Engineering, SRM University, Chennai, IN
2 Department of Electronics and Communication Engineering, SRM University, Chennai, IN

Abstract

Cluster Methodology is a technique used for enhancing human interaction with image retrieval systems by fully exploiting the similarity information. The method is used to retrieve image clusters instead of set of sequenced images. The query image and the neighboring target images which are selected according to the similarity measures are clustered and the performance of the system is evaluated on a database of about 1000 images initially; Empirical results demonstrate improved performance compared with a typical CBIR system using the same image similarity measure. Online photo-sharing has become extremely popular with Flickr which hosts hundreds of millions of pictures with diverse content. The video sharing and distribution forum YouTube has also brought in a new revolution in multimedia usage. Of late, there is renewed interest in the media about potential real-world applications of CBIR and image analysis technologies. In addition, preliminary results on images returned by Google's Image Search reveal the potential of applying cluster methodology to real world image data and integrating the new method as a part of the interface for keyword-based image retrieval systems. In this paper, we survey almost 100 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and discuss the spawning of related sub-fields in the process. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real-world.

Keywords

Content Based Image Retrieval (CBIR), Similarity Measures, Fuzzy c Means Algorithm, Clusters, Keyword Based Image Retrieval.

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