Automatic color image segmentation by dynamic region growth and multimodal merging of color and texture information

Image segmentation is a fundamental task in many computer vision applications. In this paper, we present a novel unsupervised color image segmentation algorithm that utilizes color gradients, dynamic thresholding and texture modeling algorithms in a split and merge framework. To this effect, pixels without edges are clustered and labeled individually to identify the preliminary image content. Pixels that contain higher gradients are further classified by utilizing an iterative dynamic threshold generation technique and an appropriate entropy based texture model. The proposed algorithm was demonstrated successfully on an extensive database of images and benchmarked favorably against prior art.