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.
Mercer kernels for object recognition with local features
2005-01-01
235083 byte
Conference paper
Electronic Resource
English
Explicit Embeddings for Nearest Neighbor Search with Mercer Kernels
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