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A Large Margin Learning Method for Matching Images of Natural Objects With Different Dimensions

A Large Margin Learning Method for Matching Images of Natural Objects With Different Dimensions

Haoyi Zhou, Jun Zhou, Haichuan Yang, Cheng Yan, Xiao Bai, Yunlu Liu
Copyright: © 2019 |Pages: 20
ISBN13: 9781522580546|ISBN10: 1522580549|EISBN13: 9781522580553
DOI: 10.4018/978-1-5225-8054-6.ch026
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MLA

Zhou, Haoyi, et al. "A Large Margin Learning Method for Matching Images of Natural Objects With Different Dimensions." Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2019, pp. 561-580. https://doi.org/10.4018/978-1-5225-8054-6.ch026

APA

Zhou, H., Zhou, J., Yang, H., Yan, C., Bai, X., & Liu, Y. (2019). A Large Margin Learning Method for Matching Images of Natural Objects With Different Dimensions. In I. Management Association (Ed.), Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 561-580). IGI Global. https://doi.org/10.4018/978-1-5225-8054-6.ch026

Chicago

Zhou, Haoyi, et al. "A Large Margin Learning Method for Matching Images of Natural Objects With Different Dimensions." In Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 561-580. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8054-6.ch026

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Abstract

Imaging devices are of increasing use in environmental research requiring an urgent need to deal with such issues as image data, feature matching over different dimensions. Among them, matching hyperspectral image with other types of images is challenging due to the high dimensional nature of hyperspectral data. This chapter addresses this problem by investigating structured support vector machines to construct and learn a graph-based 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 a graph matching algorithm on extracted weighted graph models. The effectiveness of this method is demonstrated through experiments on matching hyperspectral images to RGB images, and hyperspectral images with different dimensions on images of natural objects.

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