Semantic Image Retrieval Using Relevance Feedback

摘要

This paper presents optimized interactive content-based image retrieval framework based on AdaBoost learning method. 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 AdaBoost. The main significances of our system are to address the small training sample and to reduce retrieval time. Experiments are conducted on 1000 semantic colour images from Corel database to demonstrate the effectiveness of the proposed framework. These experiments employed large image database and combined RCWFs and DT-CWT texture descriptors to represent content of the images.