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- W1965565478 abstract "Relevance feedback has been developed to improve retrieval performance effectively in Content Based Image Retrieval (CBIR). This paper introduces a relevance feedback system for CBIR with both short-term relevance feedback and long-term learning. In short-term relevance feedback, query reweighting algorithm, support vector machines (SVM), and genetic algorithm are adopted. In long-term learning, the expanded-judging model with index table is used for analyzing the historical log data. Experimental results show that among short-term feedback algorithms, the SVM gets the best feedback results, and for the use of our proposed expanded-judging model in long-term learning, the recall of the retrieval system is improved more than 30% in average." @default.
- W1965565478 created "2016-06-24" @default.
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- W1965565478 date "2010-01-01" @default.
- W1965565478 modified "2023-09-25" @default.
- W1965565478 title "A Relevance Feedback System for CBIR with Long-Term Learning" @default.
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- W1965565478 doi "https://doi.org/10.1109/mines.2010.151" @default.
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