Bayesian Learning for Image Retrieva Using Multiple Features

Wang, Lei; Chan, Kap Luk

doi:10.1007/3-540-44491-2_69

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Image Retrieval Based On Multiple Features1

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“…The features with lower weight use a subset of the total database determined by the higher weight features. In [26], instead of giving every feature a weight explicitly, the importance of a feature is regulated implicitly by learning a user's perception based on Bayesian Learning. Then the process of feature combination is adaptive.…”

Section: Image Retrieval Based On Multiple Featuresmentioning

confidence: 99%

Image retrieval and classification based on MPEG-7 descriptors

Wang¹

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I would like to express my gratitude to my supervisor, Dr. Chia Liang-Tien, for his advice and support throughout the whole process of my Ph.D study. I am sincerely grateful for his guidance, understanding and patience in leading me through this effort. My research could not have been finished without insightful advice from him. I would like to extend my thanks to Dr. Deepu Rajan and Dr. Manoranjan Dash for their thorough help and supervision. They kindly assisted me in many aspects. I also would like to thank all my colleagues in Center for Multimedia and Network Technology (CeMNet), for their understanding and supports. In particular, I'd like to thank

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Section: Image Retrieval Based On Multiple Featuresmentioning

confidence: 99%

Image retrieval and classification based on MPEG-7 descriptors

Wang¹

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show abstract

Bayesian Learning for Image Retrieva Using Multiple Features

Cited by 1 publication

References 6 publications

Image retrieval and classification based on MPEG-7 descriptors

Image retrieval and classification based on MPEG-7 descriptors

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