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Content Analysis from User's Relevance Feedback for Content-Based Image Retrieval

Content Analysis from User's Relevance Feedback for Content-Based Image Retrieval

Chia-Hung Wei, Chang-Tsun Li
Copyright: © 2009 |Pages: 19
ISBN13: 9781605661742|ISBN10: 1605661740|ISBN13 Softcover: 9781616925635|EISBN13: 9781605661759
DOI: 10.4018/978-1-60566-174-2.ch010
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MLA

Wei, Chia-Hung, and Chang-Tsun Li. "Content Analysis from User's Relevance Feedback for Content-Based Image Retrieval." Artificial Intelligence for Maximizing Content Based Image Retrieval, edited by Zongmin Ma, IGI Global, 2009, pp. 216-234. https://doi.org/10.4018/978-1-60566-174-2.ch010

APA

Wei, C. & Li, C. (2009). Content Analysis from User's Relevance Feedback for Content-Based Image Retrieval. In Z. Ma (Ed.), Artificial Intelligence for Maximizing Content Based Image Retrieval (pp. 216-234). IGI Global. https://doi.org/10.4018/978-1-60566-174-2.ch010

Chicago

Wei, Chia-Hung, and Chang-Tsun Li. "Content Analysis from User's Relevance Feedback for Content-Based Image Retrieval." In Artificial Intelligence for Maximizing Content Based Image Retrieval, edited by Zongmin Ma, 216-234. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-174-2.ch010

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Abstract

An image is a symbolic representation; people interpret an image and associate semantics with it based on their subjective perceptions, which involves the user’s knowledge, cultural background, personal feelings and so on. Content-based image retrieval (CBIR) systems must be able to interact with users and discover the current user’s information needs. An interactive search paradigm that has been developed for image retrieval is machine learning with a user-in-the-loop, guided by relevance feedback, which refers to the notion of relevance of the individual image based on the current user’s subjective judgment. Relevance feedback serves as an information carrier to convey the user’s information needs / preferences to the retrieval system. This chapter not only provides the fundamentals of CBIR systems and relevance feedback for understanding and incorporating relevance feedback into CBIR systems, but also discusses several approaches to analyzing and learning relevance feedback.

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