(untitled) (International Journal of Engineering and Computer Science, 2015)
Content Based Image Retrieval (CBIR) is a traditional and developing trend in Digital Image Processing. Relevance feedback (RF) is a technique used in collecting relevant information from the user. Content-based Image Retrieval (CBIR) using Relevance Feedback systems generates image based on image features and store in database and compare input query image feature with the features stored in database. To get good results, relevance feedback techniques were incorporated into CBIR so we can achieve better performance by taking users feedback. CBIR is used combine low level features and high level semantics according to need of the user. In this work, we are using two type of methods like SVM (support vector machine) and NPRF (navigation-pattern based relevance feedback). SVM classifier is used to differentiate between relevant and irrelevant images by using low level features like color, shape, texture features. By applying SVM we may reduce the size of query search in the data base and we may apply NPRF algorithm. The NPRF uses the discovered navigation pattern and three query refinement concepts viz Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to provide a better search towards user. SVM based navigation pattern provide good quality of image retrieval in less number of feedback.