Deciding the Number of Color Histogram Bins for Vehicle Color Recognition

Authors:
Ku-Jin Kim;Sun-Mi Park;Yoo-Joo Choi
Affiliations:
-;-;-
Venue:
APSCC '08 Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference
Year:
2008

Citing 0
Cited 2

A vehicle color classification method for video surveillance system concerning model-based background subtraction

PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
A view-invariant and anti-reflection algorithm for car body extraction and color classification

Multimedia Tools and Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

Given vehicle images, we suggest a way to recognize the color of the vehicle contained in the image. The color feature of a vehicle is represented by a color histogram, and we decide the appropriate number of color histogram bins, which mainly affects the successful recognition rate. After generating the histograms, template matching is used to decide thevehicle color. In HSI (hue saturation intensity) color space, experimental results show that the partition of H, S, and I into 8, 4, 4, respectively, achieves the highest success rate up to 88.34%.