Relevance feedback (RF) is an interactive process which refines the retrievals by utilizing user's feedback history. Most researchers strive to develop new RF techniques and ignore the advantages of existing ones. We propose an image relevance reinforcement learning (IRRL) model for integrating existing RF techniques. Various integration schemes are presented and a long-term shared memory is used to exploit the retrieval experience from multiple users. Also, a concept digesting method is proposed to reduce the complexity of storage demand. The experimental results manifest that the integration of multiple RF approaches gives better retrieval performance than using one RF technique alone, and that the sharing of relevance knowledge between multiple query sessions also provides significant contributions for improvement. Further, the storage demand is significantly reduced by the concept digesting technique. This shows the scalability of the proposed model against a growing-size database.
Reinforcement learning for combining relevance feedback techniques
2003-01-01
339431 byte
Conference paper
Electronic Resource
English
Reinforcement Learning for Combining Relevance Feedback Techniques in Image Retrieval
British Library Conference Proceedings | 2003
|Retrieval Algorithm Combining Multi-Query Data Fusion with Positive Relevance Feedback
British Library Online Contents | 2005
|Relevance Feedback in Surfimage
British Library Conference Proceedings | 1998
|Relevance feedback in Surfimage
IEEE | 1998
|Image retrieval with relevance feedback
IEEE | 2000
|