@inproceedings{d92c5a6fafde473eb0b6207890457146,
title = "Learning graph model for different dimensions image matching",
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 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.",
keywords = "Graph Model, Hyperspectral Image, Matching",
author = "Haoyi Zhou and Xiao Bai and Jun Zhou and Haichuan Yang and Yun Liu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 10th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2015 ; Conference date: 13-05-2015 Through 15-05-2015",
year = "2015",
doi = "10.1007/978-3-319-18224-7\_16",
language = "英语",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "158--167",
editor = "Bin Luo and Kropatsch, \{Walter G.\} and Cheng-Lin Liu and Jian Cheng",
booktitle = "Graph-Based Representations in Pattern Recognition - 10th IAPR-TC-15 InternationalWorkshop, GbRPR 2015, Proceedings",
address = "德国",
}