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Image Segmentation using Statistical approach via Perception-based Color Information


Seon-Do Kang, Sang-Seong Park, Young-Geun Shin, Hun-Woo Yoo, Dong-Sik Jang


Vol. 8  No. 4  pp. 41-47


Color image segmentation is useful for fast retrieval in large image database. For that purpose, new image segmentation technique based on the probability of pixel distribution in the image is proposed. Color image is first divided into R, G, and B channel images. Then, pixel distribution from each of channel image is extracted to select to which it is similar among the well known probabilistic distribution function- Weibull, Exponential, Beta, Gamma, Normal, and Uniform. We use sum of least square error to measure of the quality how well an image is fitted to distribution. That P.d.f has minimum score in relation to sum of square error is chosen. Next, each image is quantized into 4 gray levels by applying thresholds to the c.d.f of the selected distribution of each channel. Finally, three quantized images are combined into one color image to obtain final segmentation result. To show the validity of the proposed method, experiments on some images are performed.


Color image, Segmentation, Probability Density Function (p.d.f), Cumulative Density Function (c.d.f)