Combining Low-level Features And High-level Semantic Image Retrieval System

The rapid development of computer,multimedia and Internet techniques has produced too large amount of images .Therefore, it becomes an urgent problem that how to find needed image efficiently in large-scale image database. Two effective ways has been proposed to solve the problem :one is content-based image retrieval technique which search target images by low-level content feature. The other is semantic image retrieval technique which in search of ways to get images' semantic information.In this paper, a lot research work has been done around three points: how to abstract and index low-level feature of images, how to retrieval images in semantic level, and how to fill the semantic gap by relevant feedback technique. Based on the research work , An efficient and practical image retrieval system is built which integrated the advantages of these techniques.In the system, key word networks and low-level feature table are both established for images, which realized double indexing; relevant feedback is also applied in the system. On one hand, it enables the system catch users' query intention on line by adjusting its similarity criterion automatically; on the other hand, it pass the annotations for relevant images, update weights between key words and images and fill the semantic networks, thus the system can study in long-term. Users can provide their query requirement in several different manners, the best retrieval result will be presented for the low-level feature and semantic network can cooperate properly. The experiment shows that the retrieval accuracy of the system is above 70% in few steps of relevant feedback (five times in average), and the performance could increase steadily with it been used.