Designing of a rigorous image retrieval system with amalgamation of artificial intelligent techniques and relevance feedback

Abstract

Retrieving out the most comparable images from huge databases is the challenging task for image retrieval systems. So, there is a great need of constructing a capable and rigorous image retrieval system. In this implementation, an exclusive and competent Content based image retrieval (CBIR) system is schemed by the integration of Color moment (CM) and Local binary pattern (LBP). A hybrid feature vector is created by the combination of these two techniques through the process of normalization. This hybrid feature vector is given as the input to the intelligent classifiers i.e. Support vector machine (SVM) and Cascade forward back propagation neural network (CFBPNN). After that, Relevance feedback (RF) technique is applied so as to get the high level information in order to reduce the semantic gap. So, here two Artificial Intelligent CBIR models are proposed, first one is (Hybrid+SVM+RF) and second is (Hybrid+CFBPNN+RF) and their performance parameters are compared. The implementations are performed on two benchmark dataset Corel-1K and Oxford flower dataset which contains 1000 and 1360 images respectively. Different parameters are figured such as accuracy, precision, average retrieval time, recall etc. The average precision obtained for the first model is 93% with Corel 1K database and 91% with Oxford flower database. And similarly for the second model, it is 97% and 94% respectively which is higher than the first model. This implemented technique is validated on both the datasets and the attained results outperforms with other related s approaches.