Yin Peng-Yeng, Bhanu Bir, Chang Kuang-Cheng, Dong Anlei
Department of Information Management, National Chi Nan University, 303 University Rd., Puli, Nantou 545, Taiwan.
IEEE Trans Pattern Anal Mach Intell. 2005 Oct;27(10):1536-51. doi: 10.1109/TPAMI.2005.201.
Relevance feedback (RF) is an interactive process which refines the retrievals to a particular query by utilizing the user's feedback on previously retrieved results. 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 in a content-based image retrieval system. 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 significantly improves the performance. Further, the storage demand is significantly reduced by the concept digesting technique. This shows the scalability of the proposed model with the increasing-size of database.
相关反馈（RF）是一个交互式过程，它通过利用用户对先前检索结果的反馈来优化针对特定查询的检索。大多数研究人员致力于开发新的RF技术，而忽略了现有技术的优势。在本文中，我们提出了一种图像相关强化学习（IRRL）模型，用于在基于内容的图像检索系统中集成现有RF技术。提出了各种集成方案，并使用长期共享内存来利用来自多个用户的检索经验。此外，还提出了一种概念消化方法以降低存储需求的复杂性。实验结果表明，多种RF方法的集成比单独使用一种RF技术具有更好的检索性能，并且多个查询会话之间的相关知识共享显著提高了性能。此外，概念消化技术显著降低了存储需求。这表明所提出的模型随着数据库规模的增加具有可扩展性。
