Article

Mercer Kernels for Object Recognition with Local Features

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CVPR '05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02

Pages 223 - 229

https://doi.org/10.1109/CVPR.2005.223

Published: 20 June 2005 Publication History

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Abstract

A new class of kernels for object recognition based on local image feature representations are introduced in this paper. These kernels satisfy the Mercer condition and incorporate multiple types of local features and semilocal constraints between them. Experimental results of SVM classifiers coupled with the proposed kernels are reported on recognition tasks with the COIL-100 database and compared with existing methods. The proposed kernels achieved competitive performance and were robust to changes in object configurations and image degradations.

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Tommasi TMallya APlummer BLazebnik SBerg ABerg T(2019)Combining Multiple Cues for Visual Madlibs Question AnsweringInternational Journal of Computer Vision10.1007/s11263-018-1096-0127:1(38-60)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s11263-018-1096-0
Shi YLi WGao YCao LShen DSingh SMarkovitch S(2017)Beyond IIDProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298239.3298461(1524-1531)Online publication date: 4-Feb-2017
https://dl.acm.org/doi/10.5555/3298239.3298461
Jiu MSahbi H(2017)Nonlinear Deep Kernel Learning for Image AnnotationIEEE Transactions on Image Processing10.1109/TIP.2017.266603826:4(1820-1832)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2666038
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Index Terms

Mercer Kernels for Object Recognition with Local Features
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision representations
  2. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Markov decision processes
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

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CVPR '05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02

June 2005

1169 pages

ISBN:0769523722

Publisher

IEEE Computer Society

United States

Publication History

Published: 20 June 2005

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Tommasi TMallya APlummer BLazebnik SBerg ABerg T(2019)Combining Multiple Cues for Visual Madlibs Question AnsweringInternational Journal of Computer Vision10.1007/s11263-018-1096-0127:1(38-60)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s11263-018-1096-0
Shi YLi WGao YCao LShen DSingh SMarkovitch S(2017)Beyond IIDProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298239.3298461(1524-1531)Online publication date: 4-Feb-2017
https://dl.acm.org/doi/10.5555/3298239.3298461
Jiu MSahbi H(2017)Nonlinear Deep Kernel Learning for Image AnnotationIEEE Transactions on Image Processing10.1109/TIP.2017.266603826:4(1820-1832)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2666038
Banerjee SChatterjee A(2017)Robust multimodal multivariate ear recognition using kernel based simultaneous sparse representationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2017.06.01164:C(340-351)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.engappai.2017.06.011
Banerjee SChatterjee A(2016)Image set based ear recognition using novel dictionary learning and classification schemeEngineering Applications of Artificial Intelligence10.1016/j.engappai.2016.05.00555:C(37-46)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1016/j.engappai.2016.05.005
Stommel MHerzog OXu W(2016)Hyperplane arrangements for the fast matching and classification of visual landmarksPattern Analysis & Applications10.1007/s10044-014-0417-319:3(621-629)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s10044-014-0417-3
Leveau VJoly ABuisson OValduriez PHauptmann ANgo CXue XJiang YSnoek CVasconcelos N(2015)Kernelizing Spatially Consistent Visual Matches for Fine-Grained ClassificationProceedings of the 5th ACM on International Conference on Multimedia Retrieval10.1145/2671188.2749328(155-162)Online publication date: 22-Jun-2015
https://dl.acm.org/doi/10.1145/2671188.2749328
Palazzo SMurabito FSpampinato CMezaris VCristani M(2014)Fish Species Identification in Real-Life Underwater ImagesProceedings of the 3rd ACM International Workshop on Multimedia Analysis for Ecological Data10.1145/2661821.2661822(13-18)Online publication date: 7-Nov-2014
https://dl.acm.org/doi/10.1145/2661821.2661822
Xu YLin MLu HCardone GLane NChen ZCampbell AChoudhury TVan Laerhoven KRoggen DGatica-Perez DFukumoto M(2013)Preference, context and communitiesProceedings of the 2013 International Symposium on Wearable Computers10.1145/2493988.2494333(69-76)Online publication date: 8-Sep-2013
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Picard DGosselin P(2013)Efficient image signatures and similarities using tensor products of local descriptorsComputer Vision and Image Understanding10.1016/j.cviu.2013.02.004117:6(680-687)Online publication date: 1-Jun-2013
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