Segmentation is a challenging and important task in image processing while developing vision based decision support systems. Color and brightness are widely used properties for extracting segments, however color information usage becomes more crucial for better region distinction, especially on outdoor scenes where brightness value makes segmentation difficult. In this study, a novel segmentation algorithm which incorporates downscaling and clustering methods has been developed to find consistent coarse regions in a given input image. The new method does not require external parameters and produces consistent segmentation results on different runs. In the algorithm, two intermediate segmentation results are obtained by feeding dissimilar downscaled image information to GDD (Gaussian Density Distance) clustering method. The outputs form two different perspectives from the same image: one shows global level color distinction, and the other shows spatial color similarity information. A merging process of these two outputs is implemented to improve the final segmentation. During the study, an experimental framework is designed for analysis of the proposed approach and its evaluation. The method is extensively tested using benchmark images. Some of the selected results are presented in the paper along with a comparative study with well-known segmentation algorithms.
