Image Retrieval System Efficiency And Optimization

Relevance feedback is an efficient improvement to the Content-Based Image Retrieval system. It narrows down the gap between low-level feature representation of an image and its semantic meaning, gradually introducing human vision property into the retrieval process. But there are some drawbacks in many conventional relevance feedback algorithms, such as the system needs too many feedback iterations, and cannot reuse the former user's feedback information.In this paper, we improve one conventional relevance feedback algorithm by introducing an updatable feature database into the improved algorithm. With this algorithm, a system can gradually embed the users' feedback information into the updatable database. At the same time, we propose a new texture description of a color image, based on multi-level resolution analysis, and use this description as part of our low-level feature vectors. The experiment on an image database, including 1 000 pictures, shows that the improved algorithm can greatly improve the retrieval precision and the convergent speed of feedback, compared with that in SIMPLIcity.