Integration of Shape Context and Semigroup Kernel in image classification

Yi Guo, Junbin Gao

Research output: Book chapter/Published conference paperConference paperpeer-review

4 Citations (Scopus)
8 Downloads (Pure)

Abstract

Shape context is a rich descriptor for shapes and can be exploited to find pointwise correspondences between shapes, and thereby to obtain shape alignment by Thin Plate Spline (TPS). It is invariant under scaling and translation and robust under small geometrical distortions and presence of outliers. These features will supply a gap of the defect of semigroup kernel for its weakness in dealing with the deformation of the image. This paper integrates these two methods by defining a new kernel on shapes and images which is the combination of the shape distance from shape context and image similarity from semigroup kernel. Experiments of SVM classification on handwritten digits showed that it outperforms other existing kernels and the result of the data visualization exhibited another successful application of this new kernel.
Original languageEnglish
Title of host publicationIEEE International Conference on Machine Learning and Cybernetics (ICMLC)
EditorsXizhao Wang, Daniel Yeung
Place of PublicationWashington DC, USA
PublisherIEEE
Pages181-186
Number of pages6
Volume1
ISBN (Electronic)142440973X
DOIs
Publication statusPublished - 2007
EventICMLC2007: 6th International Conference - Hotel Miramar, Hong Kong, China
Duration: 19 Aug 200722 Aug 2007
http://www.hitsz.edu.cn/article/view/id-18804.html (conference info)

Conference

ConferenceICMLC2007: 6th International Conference
CountryChina
CityHong Kong
Period19/08/0722/08/07
Internet address

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