Cross-view object identification using principal color transformation

Shin Yu Chen*, Jun-Wei Hsieh, Duan Yu Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a novel color correction technique for object identification across different cameras. First of all, we project the analyzed object onto the LAB color space and then find its principal color axis through the principal component analysis. Since the L axis corresponds to the intensity, we then rotate the found principal color axis for making it parallel to the L axis. After this rotation, the color distortions among different cameras can be reduced into minimum. Then, a hybrid classifier is designed for classifying objects into different categories even though they are captured under different lighting conditions. Based on a polar coordinate, a sampling technique is then proposed for extracting several important color features from A-B plane. Then, using the SVM learning algorithm, a color classifier can be trained for classifying each object into different categories. For the non-color categories, we quantize the RGB channels into different levels. Then, another classifier is obtained for classifying each gray object into its corresponding category. Since the proposed color correction scheme reduce the problem of color distortions into a minimum, each object can be well classified and identified even though they are captured across different cameras and under lighting condition.

Original languageEnglish
Title of host publication2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages2777-2781
Number of pages5
DOIs
StatePublished - 15 Nov 2010
Event2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
Duration: 11 Jul 201014 Jul 2010

Publication series

Name2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Volume6

Conference

Conference2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
CountryChina
CityQingdao
Period11/07/1014/07/10

Fingerprint Dive into the research topics of 'Cross-view object identification using principal color transformation'. Together they form a unique fingerprint.

Cite this