Mercer kernels for object recognition with local features

Author

Lyu, Siwei

Author_Institution

Dept. of Comput. Sci., Dartmouth Coll., Hanover, NH, USA

Volume

2

fYear

2005

fDate

20-25 June 2005

Firstpage

223

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.

Keywords

feature extraction; image classification; image representation; object recognition; support vector machines; visual databases; COIL-100 database; Mercer kernels; SVM classifiers; image degradations; image feature representations; object configuration; object recognition; semilocal constraints; Application software; Computer science; Computer vision; Degradation; Kernel; Noise robustness; Object recognition; Spatial databases; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on

ISSN

1063-6919

Print_ISBN

0-7695-2372-2

Type

conf

DOI

10.1109/CVPR.2005.223

Filename

1467446