A Comparative Study of Different Color Space Models Using FCM-Based Automatic GrabCut for Image Segmentation

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.

[1]  Joachim Denzler,et al.  Semantic Segmentation using GrabCut , 2012, VISAPP.

[2]  Sergio Escalera,et al.  Spatio-Temporal GrabCut human segmentation for face and pose recovery , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[3]  Mohamed F. Tolba,et al.  Automatic GrabCut for Bi-label Image Segmentation Using SOFM , 2014, IEEE Conf. on Intelligent Systems.

[4]  Carlos Lopez-Molina,et al.  A Comparison Study of Different Color Spaces in Clustering Based Image Segmentation , 2010, IPMU.

[5]  Manish Shrivastava,et al.  COLOUR IMAGE SEGMENTATION TECHNIQUES AND ISSUES: AN APPROACH , 2012 .

[6]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[7]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  S. Zulaikha Beevi,et al.  A robust fuzzy clustering technique with spatial neighborhood information for effective medical image segmentation: An efficient variants of fuzzy clustering technique with spatial information for effective noisy medical image segmentation , 2010, 2010 Second International conference on Computing, Communication and Networking Technologies.

[9]  Olivier Alata,et al.  Is there a best color space for color image characterization or representation based on Multivariate Gaussian Mixture Model? , 2009, Comput. Vis. Image Underst..

[10]  Andrew Zisserman,et al.  Humanising GrabCut: Learning to segment humans using the Kinect , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[11]  Vladimir Kolmogorov,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..

[12]  Humberto Bustince,et al.  New method to assess barley nitrogen nutrition status based on image colour analysis , 2009 .

[13]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[14]  Erkki Oja,et al.  Engineering applications of the self-organizing map , 1996, Proc. IEEE.

[15]  Yi Hu.  Human Body Region Extraction from Photos , 2007, MVA.

[16]  Rhadamés Carmona,et al.  A volume segmentation approach based on GrabCut , 2013, CLEI Electron. J..

[17]  Nicolas Vandenbroucke,et al.  Color Spaces and Image Segmentation , 2008 .

[18]  K. Karthik,et al.  Image Segmentation of Homogeneous Intensity Regions using Wavelets based Level Set , 2013 .

[19]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[20]  Nicolas Vandenbroucke,et al.  Color image segmentation by pixel classification in an adapted hybrid color space. Application to soccer image analysis , 2003, Comput. Vis. Image Underst..

[21]  Mohamed Sathik,et al.  A robust segmentation approach for noisy medical images using fuzzy clustering with spatial probability , 2012, Int. Arab J. Inf. Technol..

[22]  Claas Bontus,et al.  Reconstruction Algorithms for Computed Tomography , 2009 .

[23]  David Corrigan,et al.  Video Matting Using Motion Extended GrabCut , 2008 .

[24]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[25]  José M. Chaves-González,et al.  Detecting skin in face recognition systems: A colour spaces study , 2010, Digit. Signal Process..

[26]  H. Irshad,et al.  Image segmentation using fuzzy clustering: A survey , 2010, 2010 6th International Conference on Emerging Technologies (ICET).

[27]  Stelios Krinidis,et al.  A Robust Fuzzy Local Information C-Means Clustering Algorithm , 2010, IEEE Transactions on Image Processing.

[28]  Cheng-Jin Du,et al.  Comparison of three methods for classification of pizza topping using different colour space transformations , 2005 .

[29]  Jaime Gomez-Gil,et al.  Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA) , 2009 .