Abstract—Content-based image retrieval (CBIR) is a technology that in principle helps to organize digital picture archives by their visual content. Anything ranging from an image similarity function to a robust image annotation engine falls under the purview of CBIR. In all current approaches the one problem is the visual similarity for judging semantic similarity which is problematic between low level content and high level concepts due to the semantic gap. In order to improve the retrieval accuracy of CBIR systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reduce the ‘semantic gap’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in feature extraction for image retrieval.
Index Terms—CBIR, colour, texture and shape
Sowmya Rani is with the Dept of computer science, RVCE, Banglore.
Rajani N and Swathi Reddy are with the M. Tech (Digital comm and working),SJBIT, Banglore Rajani N’s (e-mail: firstname.lastname@example.org)
Cite: Sowmya Rani, Rajani N., and Swathi Reddy, "Comparative Study on Content Based Image Retrieval," International Journal of Future Computer and Communication vol. 1, no. 4 pp. 366-368, 2012.