Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10369764 | Signal Processing | 2005 | 5 Pages |
Abstract
Relevance feedback can be considered as a Bayesian classification problem. For retrieving images efficiently, an adaptive relevance feedback approach based on the Bayesian inference, rich get richer (RGR), is proposed. If the feedback images in current iteration are consistent with the previous ones, the images that are similar to the query target are assigned to high probabilities. Therefore, the images that are similar to the user's ideal target are emphasized step by step. The experiments showed that the average precision of RGR improves 5-20% on each interaction compared with non-RGR. When compared with MARS, the proposed approach greatly reduces the user's efforts for composing a query and captures user's intention efficiently.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Lijuan Duan, Wen Gao, Wei Zeng, Debin Zhao,