Unsupervised image segmentation by automatic gradient thresholding for dynamic region growth in the CIE L*a*b* color space

Sreenath Rao Vantaram; Eli Saber; Vincent Amuso; Mark Shaw; Ranjit Bhaskar

doi:10.1117/12.805416

10 February 2009 Unsupervised image segmentation by automatic gradient thresholding for dynamic region growth in the CIE L*a*b* color space

Sreenath Rao Vantaram, Eli Saber, Vincent Amuso, Mark Shaw, Ranjit Bhaskar

Author Affiliations +

Proceedings Volume 7240, Human Vision and Electronic Imaging XIV; 724019 (2009) https://doi.org/10.1117/12.805416
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

In this paper, we propose a novel unsupervised color image segmentation algorithm named GSEG. This Gradient-based SEGmentation method is initialized by a vector gradient calculation in the CIE L*a*b* color space. The obtained gradient map is utilized for initially clustering low gradient content, as well as automatically generating thresholds for a computationally efficient dynamic region growth procedure, to segment regions of subsequent higher gradient densities in the image. The resultant segmentation is combined with an entropy-based texture model in a statistical merging procedure to obtain the final result. Qualitative and quantitative evaluation of our results on several hundred images, utilizing a recently proposed evaluation metric called the Normalized Probabilistic Rand index shows that the GSEG algorithm is robust to various image scenarios and performs favorably against published segmentation techniques.

Citation Download Citation

Sreenath Rao Vantaram, Eli Saber, Vincent Amuso, Mark Shaw, and Ranjit Bhaskar "Unsupervised image segmentation by automatic gradient thresholding for dynamic region growth in the CIE L*a*b* color space", Proc. SPIE 7240, Human Vision and Electronic Imaging XIV, 724019 (10 February 2009); https://doi.org/10.1117/12.805416

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