Abstract
A new color image segmentation method is proposed in this paper. The proposed method is based on the human perception that in general human has attention on 3 or 4 major color objects in the image at first. Therefore, to determine the objects, three intensity distributions are constructed by sampling them randomly and sufficiently from three R, G, and B channel images. And three means are computed from three intensity distributions. Next, these steps are repeated many times to obtain three mean distribution sets. Each of these distributions comes to show normal shape based on the central limit theorem. To segment objects, each of the normal distribution is divided into 4 sections according to the standard deviation (section1 below - σ, section 2 between - σ and μ, section 3 between μ and σ, and section 4 over σ). Then sections with similar representative values are merged based on the threshold. This threshold is not chosen as constant but varies based on the difference of representative values of each section to reflect various characteristics for various images. Above merging process is iterated to reduce fine textures such as speckles remained even after the merging. Finally, segmented results of each channel images are combined to obtain a final segmentation result. The performance of the proposed method is evaluated through experiments over some images.
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References
Bimbo, A.D.: Visual Information Retrieval. Morgan Kaufman Pub., San Francisco (1999)
Smith, J.R., Chang, S.F.: Integrated spatial and feature image query. Multimedia Systems 7(2), 129–140 (1999)
Yoo, H.-W., Jang, D.-S., Jung, S.-H., Park, J.-H., Song, K.-S.: Visual information retrieval system via content-based approach. Pattern Recognition 35(3), 749–769 (2002)
Li, M., Chen, Z., Zhang, H.J.: Statistical correlation analysis in image retrieval. Pattern Recognition 35(12), 2687–2693 (2002)
Hijjatoleslami, S.A., Kittler, J.: Region growing: A new approach. IEEE Transactions on Image Processing 7(7), 1079–1084 (1998)
Tan, K.L., Ooi, B.C., Yee, C.Y.: An evaluation of color spatial retrieval techniques for large databases. Multimedia Tools and Application 14(1), 55–78 (2001)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing using MATLAB. Person Education (2004)
Hsiao, Y.-T., Chuang, C.L., Jiang, J.-A., Chien, C.-C.: A contour based image segmentation algorithm using morphological edge detection. In: Proc. IEEE Int. Conf. on System, Man and Cybernetics, pp. 2962–2967. IEEE Computer Society Press, Los Alamitos (2005)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, Englewood Cliffs (2002)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Amir, A., Lindenbaum, M.: A generic grouping algorithm and its quantitative analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(2), 168–185 (1998)
Gdalyahu, Y., Weinshall, D., Werman, M.: Self-organization in vision: Stochastic clustering for image segmentation, perceptual grouping, and image database organization. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(10), 1053–1074 (2001)
Chen, J., Pappas, T.N., Mojsilovic, A., Rogowitz, B.E.: Adaptive Perceptual Color-Texture Image Segmentation. IEEE Transactions on Image Processing 14(10), 1524–1536 (2005)
Fan, J., Yau, D.K.: Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Transactions on Image Processing 10(10), 1454–1466 (2001)
Navaon, E., Miller, O.: Color image segmentation based on adaptive local thresholds. Image and Vision Computing 23(1), 69–85 (2005)
Scheaffer, R.L., McClave, J.T.: Probability and Statistics for Engineers. Dexbury Press (1995)
Mojsilovic’, A., Kovaˇcevic’, J., Hu, J., Safranek, R.J., Ganapathy, S.K.: Matching and retrieval based on the vocabulary and grammar of color patterns. IEEE Transactions on Image Processing 1(1), 38–54 (2000)
Biederman, I.: Human image understanding: recent research and a theory. Computer Vision, Graphics, and Image Processing 32(1), 29–73 (1985)
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Kang, SD., Yoo, HW., Jang, DS. (2007). Color Image Segmentation Based on the Normal Distribution and the Dynamic Thresholding. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4705. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74472-6_30
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DOI: https://doi.org/10.1007/978-3-540-74472-6_30
Publisher Name: Springer, Berlin, Heidelberg
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