By Topic

Non-Neighboring Rectangular Feature selection using Particle Swarm Optimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Hidaka, A. ; Univ. of Tsukuba, Tsukuba, Japan ; Kurita, T.

Recently, Viola proposed a rectangular features (RFs) based classifier with high accuracy and rapid processing speed for object detection tasks. In this paper, we propose non-neighboring RFs (NNRFs) as an extension of RFs, and a particle swarm optimization (PSO) based feature selection algorithm for NNRFs. NNRFs are the pairs of arbitrary rectangular sub-regions in images, giving us huge number of candidate NNRFs for feature selection (e.g. 1.3 billion NNRFs in 19×19 pixel image). We show that PSO can select the powerful subset of NNRFs efficiently from the various candidates, and the classification accuracy is improved with the same computational cost as compared with that of Viola's method.

Published in:

Pattern Recognition, 2008. ICPR 2008. 19th International Conference on

Date of Conference:

8-11 Dec. 2008