Content-Based Image Retrieval Using Optimal Feature Combination and Relevance Feedback
With the rapid development of the multimedia technology and Internet, content-based image retrieval (CBIR) has become an active research field at present. Many researches have been done on visual features and their combinations for CBIR, but few on the performance comparison of different visual feature combinations. Therefore, in the paper, different visual feature combinations are firstly compared in retrieval experiments. Moreover, only using low-level features for CBIR cannot achieve a satisfactory measurement performance, since the users high-level semantics cannot be easily expressed by low-level features. In order to narrow the gap between user query concept and low-level features in CBIR, a multi-round relevance feedback (RF) strategy based on both support vector machine (SVM) and feature similarity is adopted to meet the users requirement. The experiment results showed that this SVM and feature similarity based relevance feedback using best feature combination can greatly improve the retrieval precision with the number of feedback increasing.
content-based image retrieval visual features combination support vector machine relevance feedback
Lijun Zhao Jiakui Tang
Yantai Institute of Coastal Zone Research Chinese Academy of Sciences Yantai, China
国际会议
太原
英文
436-442
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）