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  • Learning Graph Model for Different Dimensions Image Matching

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    ZhouPUB6.pdf (3.549Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Zhou, H
    Bai, X
    Zhou, J
    Yang, H
    Liu, Y
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2015
    Metadata
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    Abstract
    Hyperspectral imagery has been widely used in real applications such as remote sensing, agriculture, surveillance, and geological analysis. Matching hyperspectral images is a challenge task due to the high dimensional nature of the data. The matching task becomes more difficult when images with different dimensions, such as a hyperspectral image and an RGB image, have to be matched. In this paper, we address this problem by investigating structured support vector machine to learn graph model for each type of image. The graph model incorporates both low-level features and stable correspondences within images. The inherent ...
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    Hyperspectral imagery has been widely used in real applications such as remote sensing, agriculture, surveillance, and geological analysis. Matching hyperspectral images is a challenge task due to the high dimensional nature of the data. The matching task becomes more difficult when images with different dimensions, such as a hyperspectral image and an RGB image, have to be matched. In this paper, we address this problem by investigating structured support vector machine to learn graph model for each type of image. The graph model incorporates both low-level features and stable correspondences within images. The inherent characteristics are depicted by using graph matching algorithm on weighted graph models. We validate the effectiveness of our method through experiments on matching hyperspectral images to RGB images, and hyperspectral images with different dimensions.
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    Conference Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume
    9069
    Publisher URI
    http://www.nlpr.ia.ac.cn/gbr2015/
    DOI
    https://doi.org/10.1007/978-3-319-18224-7_16
    Copyright Statement
    © 2015 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
    Subject
    Computer vision
    Publication URI
    http://hdl.handle.net/10072/69691
    Collection
    • Conference outputs

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