Recognition and Shape Synthesis of 3-D Objects Based on Attributed Hypergraphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Object Recognition Using Hyper-Graphs and Ranked Local Invariant Features
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Learning Large Scale Class Specific Hyper Graphs for Object Recognition
ICIG '09 Proceedings of the 2009 Fifth International Conference on Image and Graphics
Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Tag-based social image search with visual-text joint hypergraph learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Location Discriminative Vocabulary Coding for Mobile Landmark Search
International Journal of Computer Vision
Less is More: Efficient 3-D Object Retrieval With Query View Selection
IEEE Transactions on Multimedia
Multi-view hypergraph learning by patch alignment framework
Neurocomputing
Multi-spectral dataset and its application in saliency detection
Computer Vision and Image Understanding
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Hyperspectral image classification requires a classifier which can deal with high-dimensional hyperspectral data. How to explore the relationship among different pixels in the hyperspetral image is essential for hyperspetral image classification. In this paper, we propose to formulate the correlation among pixels by using a hypergraph structure. The hypergraph is constructed by using the neighborhood clustering method, where each pixel is connected to its several neighbor pixels. We investigate the relevance scores among these pixels with a hypergraph learning procedure, and hyperspectral image classification is conducted by using these relevance scores. We conduct experiments on the Salinas scene dataset, and experimental results demonstrate that the proposed method can outperform the state-of-the-art methods.