Relevance Feedback Content-Based Image Retrieval Using Query Distribution Estimation Based on Maximum Entropy Principle
In the last few years, we have seen an upsurge of interest in content-based image retrieval (CBIR) the selection of images from a collection via features extracted from images themselves. Typically the nearest-neighbor rule is used to retrieve images from a query image. However, the underlying query distribution may not be isotropic in nature. IIence, a more sophisticated estimation for the query distribution is required. We propose a novel relevance feedback framework for image retrieval which contains two stages: (1) to estimate the query distribution based on relevance feedback information and (2) to generate a set of inquiries for relevance selection based on the Maximum Entropy Principle. We demonstrate these two stages in detail. Moreover, experiments have been performed on a trademark image database. The results show our proposed framework is effective in image retrieval with a few relevant samples.
Irwin King Zhong Jin
Department of Computer Science and Engineering,The Chinese University of Hong Kong,Shatin, New Terri Department of Computer Science,Nanjing University of Science and Technology,Nanjing, Peoples Republ
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
737-742
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）