Clustering-based Image Segmentation using Automatic GrabCut

GrabCut is one of the most powerful semi-automatic segmentation techniques. One main drawback of GrabCut is the need for user interaction in order to initialize the segmentation process. User interaction involves dragging a rectangle around the object of interest in the image to extract it. This restricts GrabCut for binary-label segmentation, where the image cannot be segmented into more than two; foreground and background segments. Unsupervised clustering is a powerful automatic tool for dividing images into a specified number of regions based on defined image features. The authors had introduced the SOFM clustering technique for GrabCut automation as a replacement to the user interaction. In this paper, K-means and Fuzzy C-means are introduced as new clustering techniques to automate GrabCut and improve the segmentation accuracy. Experimental results and comparisons with the previous results are carried out to test the efficiency of the proposed techniques in terms of segmentation quality and accuracy.

[1]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[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]  C. Loganathan,et al.  A Survey on Image Segmentation through Clustering Algorithm , 2013 .

[4]  Nizar Grira,et al.  Unsupervised and Semi-supervised Clustering : a Brief Survey ∗ , 2004 .

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

[6]  Alexandru Telea,et al.  International Conference on Computer Vision Theory and Applications (VISAPP) , 2014 .

[7]  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.

[8]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

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

[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]  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.

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

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

[14]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[15]  Yadwinder Kaur,et al.  Various Image Segmentation Techniques: A Review , 2014 .

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

[17]  Prashant L. Paikrao,et al.  A Review of Image Data Clustering Techniques , 2012 .

[18]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[20]  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.

[21]  M. Celenk.  Colour image segmentation by clustering , 1991 .

[22]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[23]  Avinash Shrivas,et al.  Color Image Segmentation Using K-Means Clustering and Otsu ’ s Adaptive Thresholding , 2014 .

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

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

[26]  Ramandeep Kaur,et al.  A Survey of Clustering Techniques , 2010 .

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

[28]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[29]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  S. Kannan,et al.  Fuzzy clustering Approach in segmentation of T 1T 2 brain MRI , 2009 .

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

[32]  Santanu Bhowmik,et al.  A Survey on Clustering Based Image Segmentation , 2012 .

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

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

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

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

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