Reducing the Dimension of Color Features Using a Naive Bayesian Classifier

Author

Park, Sun-Mi ; Kim, Ku-Jin

Author_Institution

Grad. Sch. of EECS, Kyungpook Nat. Univ., Daegu, South Korea

fYear

2009

fDate

20-22 Dec. 2009

Firstpage

1

Lastpage

6

Abstract

Color histograms are usually used as the color feature vectors for classifying the color of objects in images. We reduce the dimension of the feature vector by a factor of about 30 by using a naive Bayesian classifier, and use the resulting feature vectors with a support vector machine to recognize vehicle colors. Experiments show that the recognition rate is close to that achieved with the original large feature vectors, while recognition time is reduced by a factor of more than 30. We also show that our method outperforms principal component analysis.

Keywords

Bayes methods; image classification; image colour analysis; object recognition; principal component analysis; support vector machines; Naive Bayesian classifier; color feature vectors; color histograms; principal component analysis; support vector machine; vehicle color recognition; Automotive engineering; Bayesian methods; Histograms; Image classification; Image color analysis; Image recognition; Pattern recognition; Principal component analysis; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Ubiquitous Information Technologies & Applications, 2009. ICUT '09. Proceedings of the 4th International Conference on

Conference_Location

Fukuoka

ISSN

1976-0035

Print_ISBN

978-1-4244-5131-9

Type

conf

DOI

10.1109/ICUT.2009.5405735

Filename

5405735