Deciding the Number of Color Histogram Bins for Vehicle Color Recognition

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 generatin...

Full description

Saved in:
Bibliographic Details
Published in2008 IEEE Asia-Pacific Services Computing Conference pp. 134 - 138
Main Authors Ku-Jin Kim, Sun-Mi Park, Yoo-Joo Choi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2008
Subjects
Cameras
Colored noise
Feature extraction
Histograms
Image classification
Image recognition
Image retrieval
Image segmentation
Pixel
Vehicles
Online AccessGet full text

Cover

More Information
Summary: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 the vehicle 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%.
DOI:10.1109/APSCC.2008.207