TY - JFULL AU - Pushpa B. Patil and Manesh B. Kokare PY - 2013/2/ TI - Composite Relevance Feedback for Image Retrieval T2 - International Journal of Computer and Information Engineering SP - 140 EP - 147 VL - 7 SN - 1307-6892 UR - https://publications.waset.org/pdf/17351 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 73, 2013 N2 - This paper presents content-based image retrieval (CBIR) frameworks with relevance feedback (RF) based on combined learning of support vector machines (SVM) and AdaBoosts. The framework incorporates only most relevant images obtained from both the learning algorithm. To speed up the system, it removes irrelevant images from the database, which are returned from SVM learner. It is the key to achieve the effective retrieval performance in terms of time and accuracy. The experimental results show that this framework had significant improvement in retrieval effectiveness, which can finally improve the retrieval performance. ER -