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2000
IEEE

Non-linear Relevance Feedback: Improving the Performance of Content-Based Retrieval Systems

8 years 10 months ago
Non-linear Relevance Feedback: Improving the Performance of Content-Based Retrieval Systems
In this paper, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of content-based retrieval systems. In particular, the human is considered as part of the retrieval process in an interactive framework, who evaluates the results provided by the system so that the system automatically updated its performance based on the users' feedback. An adaptively trained neural network architecture is used for implementing the non- linear feedback. The weight adaptation is performed in such a way that the network output satisfies the users' selection as much as possible, while simultaneously providing a minimal degradation over all previous data. Experimental results indicates that the proposed method yields better performance compared to linear relevance feedback mechanism.
Nikolaos D. Doulamis, Anastasios D. Doulamis, Stef
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where ICMCS
Authors Nikolaos D. Doulamis, Anastasios D. Doulamis, Stefanos D. Kollias
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