QUT ePrints

Implementation of Kernel Methods on the GPU

Ohmer, Julius F., Maire, Frederic D., & Brown, Ross A. (2005) Implementation of Kernel Methods on the GPU. In 8th International Conference on Digital Image Computing: Techniques and Applications (DICTA), December, Carins, Australia.

View at publisher

Abstract

Kernel methods such as kernel principal component analysis and support vector machines have become powerful tools for pattern recognition and computer vision. Unfortunately the high computational cost of kernel methods is a limiting factor for real-time classification tasks when running on the CPU of a standard PC. Over the last few years, commodity Graphics Processing Units (GPU) have evolved from fixed graphics pipeline processors into more flexible and powerful data-parallel processors. These stream processors are capable of sustaining computation rates of greater than ten times that of a single CPU. GPUs are inexpensive and are becoming ubiquitous (desktops, laptops, PDAs, cell phones). In this paper, we present a face recognition system based on kernel methods running on the GPU. This GPU implementation is twenty eight times faster than the same optimized application running on the CPU.

Impact and interest:

4 citations in Scopus
Search Google Scholar™

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

465 since deposited on 04 Jan 2006
65 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 2964
Item Type: Conference Paper
Keywords: Kernel methods, GPGPU
DOI: 10.1109/DICTA.2005.48
ISBN: 0-7695-2467-2
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Graphics (080103)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Divisions: Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2005 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 04 Jan 2006
Last Modified: 29 Feb 2012 23:11

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page