Comparison of perceptual color spaces for natural image segmentation tasks

Fernando E. Correa-Tome; Raul E. Sanchez-Yanez; Victor Ayala-Ramirez

doi:10.1117/1.3651799

1 November 2011 Comparison of perceptual color spaces for natural image segmentation tasks

Fernando E. Correa-Tome, Raul E. Sanchez-Yanez, Victor Ayala-Ramirez

Author Affiliations +

Optical Engineering, Vol. 50, Issue 11, 117203 (November 2011). https://doi.org/10.1117/1.3651799

Abstract

Color image segmentation largely depends on the color space chosen. Furthermore, spaces that show perceptual uniformity seem to outperform others due to their emulation of the human perception of color. We evaluate three perceptual color spaces, CIELAB, CIELUV, and RLAB, in order to determine their contribution to natural image segmentation and to identify the space that obtains the best results over a test set of images. The nonperceptual color space RGB is also included for reference purposes. In order to quantify the quality of resulting segmentations, an empirical discrepancy evaluation methodology is discussed. The Berkeley Segmentation Dataset and Benchmark is used in test series, and two approaches are taken to perform the experiments: supervised pixelwise classification using reference colors, and unsupervised clustering using k-means. A majority filter is used as a postprocessing stage, in order to determine its contribution to the result. Furthermore, a comparison of elapsed times taken by the required transformations is included. The main finding of our study is that the CIELUV color space outperforms the other color spaces in both discriminatory performance and computational speed, for the average case.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Fernando E. Correa-Tome, Raul E. Sanchez-Yanez, and Victor Ayala-Ramirez "Comparison of perceptual color spaces for natural image segmentation tasks," Optical Engineering 50(11), 117203 (1 November 2011). https://doi.org/10.1117/1.3651799

Published: 1 November 2011

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available