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

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.