Document Type
Technical Report
Publication Date
10-1-2004
Technical Report Number
TR2004-520
Abstract
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 image degradations.
Dartmouth Digital Commons Citation
Lyu, Siwei, "Mercer Kernels for Object Recognition with Local Features" (2004). Computer Science Technical Report TR2004-520. https://digitalcommons.dartmouth.edu/cs_tr/256
Comments
Submitted to CVPR 2005.