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2003
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Reinforcement Learning for Combining Relevance Feedback Techniques

9 years 2 months ago
Reinforcement Learning for Combining Relevance Feedback Techniques
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. In this paper, 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 grow...
Peng-Yeng Yin, Bir Bhanu, Kuang-Cheng Chang, Anlei
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where ICCV
Authors Peng-Yeng Yin, Bir Bhanu, Kuang-Cheng Chang, Anlei Dong
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