Automatic image segmentation by dynamic region growth and multiresolution merging

Authors:
Luis Garcia Ugarriza;Eli Saber;Sreenath Rao Vantaram;Vincent Amuso;Mark Shaw;Ranjit Bhaskar
Affiliations:
Zoran Corporation, Burlington, MA;Rochester Institute of Technology, Rochester, NY;Rochester Institute of Technology, Rochester, NY;Rochester Institute of Technology, Rochester, NY;Hewlett Packard Company, Boise, ID;Hewlett Packard Company, Boise, ID
Venue:
IEEE Transactions on Image Processing
Year:
2009

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Abstract

Image segmentation is a fundamental task in many computer vision applications. In this paper, we propose a new unsupervised color image segmentation algorithm, which exploits the information obtained from detecting edges in color images in the CIE L * a * b * color space. To this effect, by using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify some initial portion of the input image content. Elements that contain higher gradient densities are included by the dynamic generation of clusters as the algorithm progresses. Texture modeling is performed by color quantization and local entropy computation of the quantized image. The obtained texture and color information along with a region growth map consisting of all fully grown regions are used to perform a unique multiresolution merging procedure to blend regions with similar characteristics. Experimental results obtained in comparison to published segmentation techniques demonstrate the performance advantages of the proposed method.