Abstract
This paper proposes a new image retrieval strategy based on the optimal feature subset that is iteratively learned from the query image. The optimal feature set that can well describe the essential properties of the query image with respect to a retrieved image database is obtained from reinforcement learning procedure with the help of human-computer interaction. Through human-computer interaction, user can provide similarity evaluation between the query and retrieved images, which actually gives the relevance feedback for a contend-based image retrieval method, and further serves as environmental rewards to feature set evolution actions in reinforcement learning procedure. Experiment results show the effectiveness of the proposed method.
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Su, J., Liu, F., Luo, Z. (2005). Evolving Optimal Feature Set by Interactive Reinforcement Learning for Image Retrieval. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_131
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DOI: https://doi.org/10.1007/11427445_131
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
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