Abstract
In many of the computer vision applications, color-to-grayscale conversion algorithms are required to preserve the salient features of the color images, such as brightness, contrast and structure of the color image. The traditional color-to-grayscale conversion algorithms such as National Television Standards Committee (NTSC) may produce mediocre images for visual observation. However, these NTSC grayscale images are not tailored for classification purposes because the objective of NTSC is not to obtain discriminative images. For image classification problems, we present a novel color-to-grayscale conversion method based on genetic algorithm (GA). By using the GA, the color image conversion coefficients are optimized to generate more discriminative grayscale images to decrease the error in image classification problems. In order to analyze the effectiveness of the proposed method, all experimental results are compared with traditional NTSC, equal and Karhunen–Loeve-based color-to-grayscale optimization methods. It is observed that the proposed method converges to more discriminative grayscale images as compared to traditional methods.
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References
Liu, C., Yang, J.: ICA color space for pattern recognition. IEEE Trans. Neural Netw. 20(2), 248–257 (2009)
Yang, J., Liu, C.: Color image discriminant models and algorithms for face recognition. IEEE Trans. Neural Netw. 19(12), 2088–2098 (2008)
Hsu, R.L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 696–706 (2002)
Li, Y., Lu, H., Zhang, L., Yang, S., Serikawa, S.: Color image segmentation using fast density-based clustering method. Future Commun. Comput. Control Manag. 141, 593–598 (2012)
Stokman, H., Gevers, T.: Selection and fusion of color models for image feature detection. IEEE Trans. Pattern Anal. Mach. Intell. 29, 371–381 (2007)
Kerr, D.A.: Chrominance subsampling in digital images. http://dougkerr.net/Pumpkin/articles/Subsampling. Accessed Mar 30, 2014
Dong, G., Xie, M.: Color clustering and learning for image segmentation based on neural networks. IEEE Trans. Neural Netw. 16, 925–936 (2005)
Vandenbroucke, N., Macaire, L., Postaire, J.-G.: Color image segmentation by pixel classification in an adapted hybrid color space: Application to soccer image analysis. Comput. Vis. Image Underst. 90, 190–216 (2003)
Menesatti, P., Angelini, C., Pallottino, F., Antonucci, F., Aguzzi, J., Costa, C.: RGB color calibration for quantitative image analysis: the 3D thin-plate spline warping approach. Sensors 12, 7063–7079 (2012)
Di, W., Da-Wen, S.: Colour measurements by computer vision for food quality control—a review. Trends Food Sci. Technol. 29, 5–20 (2013)
deQueiroz, R.L., Braun, K.M.: Color to gray and back: color embedding into textured gray images. IEEE Trans. Image Process. 15, 1464–1470 (2006)
Horiuchi, T., Nohara, F., Tominaga, S.: Accurate reversible color-to-gray mapping algorithm without distortion conditions. Pattern Recogn. Lett. 31, 2405–2414 (2010)
Hua, X.: Human–computer interactions for converting color images to gray. Neurocomputing 85, 1–5 (2012)
Lissner, I., Preiss, J., Urban, P., Lichtenauer, M.S., Zolliker, P.: Image-difference prediction: from grayscale to color. IEEE Trans. Image Process. 22, 435–446 (2013)
Pascale, D.: A review of RGB color spaces. Technical Report. The Babel Color Company (2003)
Faroudja, Y.C.: NTSC and beyond. IEEE Trans. Consum. Electron. 34, 166–178 (1988)
Bala, R., Eschbach, R.: Spatial color-to-grayscale transformation preserving chrominance edge information. In: Proceedings of the IS & T/SID Color Imaging Conference, pp. 82–86 (2004)
Rasche, K., Geist, R., Westall, J.: Detail preserving reproduction of color images for monochromats and dichromats. IEEE Comput. Graph. Appl. 25(3), 22–30 (2005)
Smith, K., Landes, P., Thollot, J., Myszkowsky, K.: Apparent greyscale: a simple and fast conversion to perceptually accurate images and video. Comput. Graph. Forum 27, 193–200 (2008)
Grundland, M., Dodgson, N.A.: The decolorize algorithm for contrast enhancing, color to grayscale conversion. Technical Report Ucam-cl-tr-649, University of Cambridge (2005)
Gooch, A., Olsen, S., Tumblin, J., Gooch, B.: Color2gray: salience-preserving color removal. ACM Trans. Graph. 22, 634–639 (2005)
Kim, Y., Jang, C., Demouth, J., Lee, S.: Robust color-to-gray via nonlinear global mapping. ACM Trans. Graph. 28, 161–165 (2009)
Lu, J., Plataniotis, K.N.: On conversion from color to gray-scale images for face detection. In: IEEE Computer Vision and Pattern Recognition Workshops, pp. 114–119 (2009)
Jones, C.F., Abbott, A.L.: Optimization of color conversion for face recognition. EURASIP J. Appl. Signal Process. 4, 522–529 (2004)
Lim, W.H., Isa, N.A.M.: Color to grayscale conversion based on neighborhood pixels effect approach for digital image. In: ELECO 2011 7th International Conference on Electrical and Electronics Engineering, Bursa, Turkey, pp. 1–4 (2011)
Chang, H., Xu, R., Zhang, L.: Equal-width partitioning roulette wheel selection in genetic algorithm. In: Technologies and Applications of Artificial Intelligence Conference (TAAI), Tainan, pp. 62–67, 16–18 (2012)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples an incremental Bayesian approach tested on 101 object categories. In: IEEE CVPR Workshop of Generative Model Based Vision (WGMBV), 27-02 (2004)
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This work has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK, Grant No. 110E238).
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Güneş, A., Kalkan, H. & Durmuş, E. Optimizing the color-to-grayscale conversion for image classification. SIViP 10, 853–860 (2016). https://doi.org/10.1007/s11760-015-0828-7
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DOI: https://doi.org/10.1007/s11760-015-0828-7