A novel matrix norm based Gaussian kernel for feature extraction of images

Jun Bao Li, Shu Chuan Chu, Jeng Shyang Pan, Jiun Huei Ho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Gaussian kernel is widely used in Support Vector Machines and many other kernel methods, and it is most often deemed to provide a local measure of similarity between vectors, which causes large storage requirements and large computational effort for transforming images to vectors owing to its viewing images as vectors. A novel matrix norm based Gaussian kernel (M-Gaussian kernel) which views images as matrices is proposed to solve the problem. Experiments conducted on ORL face database show the effectiveness of the proposed M-Gaussian kernel.

Original languageEnglish
Title of host publicationProceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006
Place of PublicationUnited States of America
Pages305-308
Number of pages4
DOIs
Publication statusPublished - 18 Dec 2006
Externally publishedYes
Event2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006 - Pasadena, CA, United States
Duration: 18 Dec 200620 Dec 2006

Publication series

NameProceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006

Conference

Conference2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006
CountryUnited States
CityPasadena, CA
Period18/12/0620/12/06

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    Li, J. B., Chu, S. C., Pan, J. S., & Ho, J. H. (2006). A novel matrix norm based Gaussian kernel for feature extraction of images. In Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006 (pp. 305-308). [4041724] (Proceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006).. https://doi.org/10.1109/IIH-MSP.2006.265004