Abstract
GrabCut is one of the powerful color image segmentation techniques. One main disadvantage of GrabCut is the need for initial user interaction to initialize the segmentation process which classifies it as a semi-automatic technique. The paper presents the use of Fuzzy C-means clustering as a replacement of the user interaction for the GrabCut automation. Several researchers concluded that no single color space model can produce the best results of every image segmentation problem. This paper presents a comparative study of different color space models using automatic GrabCut for the problem of color image segmentation. The comparative study includes the test of five color space models; RGB, HSV, XYZ, YUV and CMY. A dataset of different 30 images are used for evaluation. Experimental results show that the YUV color space is the one generating the best segmentation accuracy for the used dataset of images.
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References
Karthik, K., Hrushikesh, P.: Image segmentation of homogeneous intensity regions using wavelets based level set. International Journal of Emerging Technology and Advanced Engineering. 3(10), 215–219 (2013)
Lalitha, M., Kiruthiga, M., Loganathan, C.: A survey on image segmentation through clustering algorithm. International Journal of Science and Research (IJSR). 2(2), 348–358 (2013)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. 3rd ed., Prentice-Hall, Inc. (2006)
Sharma, N., Mishra, M., Shrivastava, M.: Colour image segmentation techniques and issues: an approach. International Journal of Scientific & Technology Research. 1(4), 9–12 (2012)
Busin, L., Vandenbroucke, N., Macaire, L.: Color spaces and image segmentation. Advances in imaging and electron physics 151(1), 1 (2008)
Rother, C., Kolmogorov, V., Blake, A.: GrabCut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)
Boykov, Y., Jolly, M.-P.: Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images. In: 8th IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 105–112 (2001)
Khattab, D., Ebied, H.M., Hussein, A.S., Tolba, M.F.: Automatic GrabCut for bi-label image segmentation using SOFM. In: Intelligent Systems’ 2014, pp. 579–592. Springer (2015)
Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering applications of the self-organizing map. Proceedings of the IEEE 84(10), 1358–1384 (1996)
Haykin, S.S.: Neural Networks and Learning Machines, vol. 3. Prentice Hall, New York (2009)
Bezdek, J.C.: Pattern Recognition With Fuzzy Objective Function Algorithms. Kluwer Academic Publishers (1981)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics. 3(3), 32–57 (1973)
Gulshan, V., Lempitsky, V.S., Zisserman, A.: Humanising GrabCut: Learning to segment humans using the Kinect. In: IEEE International Conference on Computer Vision (ICCV Workshops), pp. 1127–1133 (2011)
Hernández, A., Reyes, M., Escalera, S., Radeva, P.: Spatio-Temporal GrabCut human segmentation for face and pose recovery. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 33–40 (2010)
Hu, Y., Human Body Region Extraction from Photos. In: MVA, pp. 473–476 (2007)
Corrigan, D., Robinson, S., Kokaram, A.: Video matting using motion extended GrabCut. In: IET European Conference on Visual Media Production (CVMP), pp. 3–3. London, UK (2008)
Göring, C., Fröhlich, B., Denzler, J.: Semantic segmentation using GrabCut. In: Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), pp. 597–602 (2012)
Ramírez, J., Temoche, P., Carmona, R.: A volume segmentation approach based on GrabCut. CLEI Electronic Journal 16(2) (2013)
Naz, S., Majeed, H., Irshad, H.: Image segmentation using fuzzy clustering: a survey. In: 6th International Conference on Emerging Technologies (ICET), pp. 181–186 (2010)
Krinidis, S., Chatzis, V.: A robust fuzzy local information C-means clustering algorithm. IEEE Transactions on Image Processing 19(5), 1328–1337 (2010)
Beevi, S.Z., Sathik, M.M., Senthamaraikannan, K.: A robust fuzzy clustering technique with spatial neighborhood information for effective medical image segmentation. International Journal of Computer Science and Information Security (IJCSIS) 7(3), 132–138 (2010)
Kannan, S., Ramathilagam, S., Pandiyarajan, R., Sathya, A.: Fuzzy clustering Approach in segmentation of T1-T2 brain MRI. Aceee International Journal on signal & Image Processing 1(2), 43 (2010)
Beevi, Z., Sathik, M.: A Robust Segmentation Approach for Noisy Medical Images Using Fuzzy Clustering With Spatial Probability. The International Arab Journal of Information Technology 29(37), 74–83 (2012)
Alata, O., Quintard, L.: Is there a best color space for color image characterization or representation based on Multivariate Gaussian Mixture Model? Computer Vision and Image Understanding 113(8), 867–877 (2009)
Pagola, M., Ortiz, R., Irigoyen, I., Bustince, H., Barrenechea, E., Aparicio-Tejo, P., Lamsfus, C., Lasa, B.: New method to assess barley nitrogen nutrition status based on image colour analysis: Comparison with SPAD-502. Computers and electronics in agriculture 65(2), 213–218 (2009)
Jurio, A., Pagola, M., Galar, M., Lopez-Molina, C., Paternain, D.: A comparison study of different color spaces in clustering based image segmentation. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications, pp. 532–541. Springer (2010)
Chaves-González, J.M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Detecting skin in face recognition systems: A colour spaces study. Digital Signal Processing 20(3), 806–823 (2010)
Du, C.-J., Sun, D.-W.: Comparison of three methods for classification of pizza topping using different colour space transformations. Journal of food engineering 68(3), 277–287 (2005)
Ruiz-Ruiz, G., Gómez-Gil, J., Navas-Gracia, L.: Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA). Computers and electronics in agriculture 68(1), 88–96 (2009)
Vandenbroucke, N., Macaire, L., Postaire, J.-G.: Color image segmentation by pixel classification in an adapted hybrid color space. Application to soccer image analysis. Computer Vision and Image Understanding 90(2), 190–216 (2003)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: 8th IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 416–423 (2001)
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Khattab, D., Ebied, H.M., Hussein, A.S., Tolba, M.F. (2015). A Comparative Study of Different Color Space Models Using FCM-Based Automatic GrabCut for Image Segmentation. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9155. Springer, Cham. https://doi.org/10.1007/978-3-319-21404-7_36
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DOI: https://doi.org/10.1007/978-3-319-21404-7_36
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