Interactive content-based texture image retrieval

Due to the semantic gap between low-level image features and high level concepts, content-Based image retrieval (CBIR) systems are incapable to provide the effective results to the user. To address this problem, we have presented a framework for effective image retrieval by proposing a novel idea of cumulative learning using Support Vector Machines (SVM). It creates a knowledge base model to increase the training samples by simply accumulating the samples based on user interactions. As we know relevance feedback (RF) is online process, so we have optimized the learning process by considering the most positive image selection on each feedback iteration. To learn the system we have used SVM. The main significances of our system are to address the small training sample and to reduce retrieval time. Experiments are conducted on 1856 texture images to demonstrate the effectiveness of the proposed framework.