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In this paper, we propose a new class of kernels for object
recognition based on local image feature representations. Formal
proofs are given to show that these kernels satisfy the Mercer
condition and reflect similarities between sets of local features. In
addition, multiple types of local features and semilocal constraints
are incorporated to reduce mismatches between local features, thus
further improve the classification performance. Experimental results
of SVM classifiers coupled with the proposed kernels are reported on
ecognition tasks with the standard COIL-100 database and compared
with existing methods. The proposed kernels achieved satisfactory
performance and were robust to changes in object configurations and
Submitted to CVPR 2005.
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
Siwei Lyu, "Mercer Kernels for Object Recognition with Local Features." Dartmouth Computer Science Technical Report TR2004-520, October 2004.
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