Review on Image Retrieval Systems

In this paper, we have reviewed and analysed different image retrieval systems. The purpose of this survey however, is to provide an overview of the functionality of temporary image retrieval systems in terms of technical aspects: querying, relevance feedback, features, matching measures, indexing data structures, and result presentation. We have reviewed different techniques like text based retrieval, content based retrieval, image annotation to get images captured by digital camera. The classification techniques such as k-KNN,SVM,Decision stump, Manifold Ranking, Hash Encoding Algorithm followed by a suitable relevant feedback model via cross domain learning , GMISVM , Laplacian Regularized Least Squares(LapRLS), Search Result Clustering(SRC)Algorithm , Biased Discriminative Euclidean embedding (BDEE)to refine the image retrieval result of consumer photos. After thorough study, this review also claims that most systems uses low level features and only few uses high level semantically meaningful features and the image retrieval results affect due to this semantic gap. The semantic gap is often regarded as a major problem in the field of image retrieval research. The comparative chart presents the details of different image retrieval system and addresses the factors to be considered for evaluation of results.