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Robust Region Descriptors for Shape Classification
Lin C.; Pun C.-M.
Conference Name13th International Conference Computer Graphics, Imaging and Visualization
Source PublicationProceedings - Computer Graphics, Imaging and Visualization: New Techniques and Trends, CGiV 2016
Conference Date29 March -1 April, 2016
Conference PlaceBeni Mellal city

A novel scheme for efficient shape classification using region descriptors and extreme learning machine with kernels is proposed. The skeleton and boundary of the input shape image are first extracted. Then the boundary is simplified to remove noise and minor variations. Finally, region descriptors for the local skeleton, and the simplified shape signature are constructed to form a hybrid feature vector. Training and classification are then performed using kernel extreme learning machine (k-ELM) for efficient shape classification. Experimental results show that the proposed scheme is very fast and can archive higher classification accuracy on the challenging MPEG-7 dataset, outperforming existing state-of-the-art methods.

KeywordContour K-elm Region Descriptor Shape Classification Signature Skeleton
URLView the original
WOS Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology
WOS SubjectComputer Science, Software Engineering ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS IDWOS:000386281600047
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Lin C.,Pun C.-M.. Robust Region Descriptors for Shape Classification[C],2016:269-272.
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