Reinforcement learning for combining relevance feedback techniques

Peng-Yeng Yin, Bhanu, Kuang-Cheng Chang, Dong
2003 Proceedings Ninth IEEE International Conference on Computer Vision  
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
more » ... thod 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.
doi:10.1109/iccv.2003.1238390 dblp:conf/iccv/YinBCD03 fatcat:mtsn24yv2vgyxa3chv4pvyc57a