A new approach for color image segmentation based on color mixture

• Published in: Machine vision and applications Vol. 24; no. 3; pp. 607 - 618
• Main Authors: Severino, Osvaldo, Gonzaga, Adilson
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01-04-2013; Springer; Springer Nature B.V
• Subjects: Applied sciences; Artificial intelligence; Color; Color imagery; Communications Engineering; Computer Science; Computer science; control theory; systems; Cones; Digital imaging; Exact sciences and technology; High-speed machining; Image processing; Image Processing and Computer Vision; Image segmentation; Methodology; Networks; Normal distribution; Oil painting; Original Paper; Pattern Recognition; Pattern recognition. Digital image processing. Computational geometry; Pixels; Planes; Retina; Saturation (color); Vision systems; Weight; Image bit-plane; Color mixture; HSM color space; Human skin segmentation; Color image processing; Biometry; Biometrics; Image processing; Gaussian distribution; Quantization; Retina; Modeling; Classification; Binary image; Overlay; Cone; Saturation; Digital image; Paint; Color image; Image segmentation; Cube; Color space; Skin; Pixel
• ISSN: 0932-8092; 1432-1769
• DOI: 10.1007/s00138-011-0395-z

The aim of this paper is to propose a new methodology for color image segmentation. We have developed an image processing technique, based on color mixture, considering how painters do to overlap layers of various hues of paint on creating oil paintings. We also have evaluated the distribution of cones in the human retina for the interpretation of these colors, and we have proposed a schema for the color mixture weight. This method expresses the mixture of black, blue, green, cyan, red, magenta, yellow and white colors quantified by the binary weight of the color that makes up the pixels of an RGB image with 8 bits per channel. The color mixture generates planes that intersect the RGB cube, defining the HSM (Hue, Saturation, Mixture) color space. The position of these planes inside the RGB cube is modeled, based on the distribution of r, g and b cones of the human retina. To demonstrate the applicability of the proposed methodology, we present in this paper, the segmentation of “human skin” or “non-skin” pixels in digital color images. The performance of the color mixture was analyzed by a Gaussian distribution in the HSM, HSV and YCbCr color spaces. The method is compared with other skin/non-skin classifiers. The results demonstrate that our approach surpassed the performance of all compared methodologies. The main contributions of this paper are related to a new way for interpreting color of binary images, taking into account the bit-plane levels and the application in image processing techniques.