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dc.contributor.authorZhou, H
dc.contributor.authorZhou, J
dc.contributor.authorYang, H
dc.contributor.authorYan, C
dc.contributor.authorBai, X
dc.contributor.authorLiu, Y
dc.contributor.editorJ. Zhou, X. Bai and T. Caelli
dc.description.abstractImaging 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.
dc.publisherInformation Science Reference
dc.publisher.placeUnited States
dc.relation.ispartofbooktitleComputer Vision and Pattern Recognition in Environmental Informatics
dc.subject.fieldofresearchComputer vision
dc.titleA Large Margin Learning Method for Matching Images of Natural Objects with Different Dimensions
dc.typeBook chapter
dc.type.descriptionB1 - Chapters
dc.type.codeB - Book Chapters
gro.hasfulltextNo Full Text
gro.griffith.authorZhou, Jun
gro.griffith.authorYan, Cheng

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