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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    Title :

    Mercer kernels for object recognition with local features


    Contributors:
    Siwei Lyu, (author)


    Publication date :

    2005-01-01


    Size :

    235083 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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