Image segmentation and adaptive superpixel generation based on harmonic edge-weighted centroidal Voronoi tessellation

In this paper, we extend the basic edge-weighted centroidal Voronoi tessellation (EWCVT) for image segmentation to a new advanced model, namely harmonic edge-weighted centroidal Voronoi tessellation (HEWCVT). This extended model introduces a harmonic form of clustering energy by combining the image intensity with cluster boundary information. Improving upon the classic centroidal Voronoi tessellation (CVT) and EWCVT methods, the HEWCVT algorithm can not only overcome the sensitivity to the seed point initialisation and noise, but also improve the accuracy and stability of clustering results, as verified in several types of images. We then present an adaptive superpixel generation algorithm based on HEWCVT. First, an innovative initial seed sampling method based on quadtree decomposition is introduced, and the image is divided into small adaptive segments according to a density function. Then, the local HEWCVT algorithm is applied to generate adaptive superpixels. The presented algorithm is capable of generating adaptive superpixels while preserving local image features efficiently.

[1]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[3]  Yao-Tien Chen,et al.  A level set method based on the Bayesian risk for medical image segmentation , 2010, Pattern Recognit..

[4]  Qiang Du,et al.  Probabilistic methods for centroidal Voronoi tessellations and their parallel implementations , 2002, Parallel Comput..

[5]  Antti Oulasvirta,et al.  Computer Vision – ECCV 2006 , 2006, Lecture Notes in Computer Science.

[6]  R. Maini.  Study and Comparison of Various Image Edge Detection Techniques , 2004 .

[7]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  M. Emre Celebi.  Real-Time Implementation of Order-Statistics Based Directional Filters , 2009, IET Image Process..

[9]  Alan C. Evans,et al.  BrainWeb: Online Interface to a 3D MRI Simulated Brain Database , 1997 .

[10]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[11]  Rui Seara,et al.  Image segmentation by histogram thresholding using fuzzy sets , 2002, IEEE Trans. Image Process..

[12]  Qiang Du,et al.  Centroidal Voronoi Tessellations: Applications and Algorithms , 1999, SIAM Rev..

[13]  Umar Mohammed,et al.  Superpixel lattices , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Tony F. Chan,et al.  ACTIVE CONTOUR and SEGMENTATION MODELS USING GEOMETRIC PDE's for MEDICAL IMAGING ? , 2002 .

[15]  Qiang Du,et al.  Constrained Centroidal Voronoi Tessellations for Surfaces , 2002, SIAM J. Sci. Comput..

[16]  Jie Wang,et al.  VCells: Simple and Efficient Superpixels Using Edge-Weighted Centroidal Voronoi Tessellations , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Nidhi Chandrakar,et al.  Study and comparison of various image edge detection techniques , 2012 .

[18]  N. Senthilkumaran,et al.  Image Segmentation - A Survey of Soft Computing Approaches , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[19]  Lili Ju,et al.  A fuzzy edge-weighted centroidal Voronoi tessellation model for image segmentation , 2016, Comput. Math. Appl..

[20]  Qiang Du,et al.  Centroidal Voronoi Tessellation Algorithms for Image Compression, Segmentation, and Multichannel Restoration , 2006, Journal of Mathematical Imaging and Vision.

[21]  Agus Zainal Arifin,et al.  Image segmentation by histogram thresholding using hierarchical cluster analysis , 2006, Pattern Recognit. Lett..

[22]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[23]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Daoqiang Zhang,et al.  Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation , 2007, Pattern Recognit..

[25]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Witold Pedrycz,et al.  Fuzzy clustering with partial supervision , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[27]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[28]  D. Dimiduk,et al.  3D microstructural characterization of nickel superalloys via serial-sectioning using a dual beam FIB-SEM , 2006 .

[29]  Sven J. Dickinson,et al.  TurboPixels: Fast Superpixels Using Geometric Flows , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[31]  Paria Mehrani,et al.  Superpixels and Supervoxels in an Energy Optimization Framework , 2010, ECCV.

[32]  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.

[33]  B. S. Manjunath,et al.  EdgeFlow: a technique for boundary detection and image segmentation , 2000, IEEE Trans. Image Process..

[34]  Jie Wang,et al.  An Edge-Weighted Centroidal Voronoi Tessellation Model for Image Segmentation , 2009, IEEE Transactions on Image Processing.

[35]  智一 吉田,et al.  Efficient Graph-Based Image Segmentationを用いた圃場図自動作成手法の検討 , 2014 .

[36]  Frank Nielsen,et al.  On weighting clustering , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[38]  Tzong-Jer Chen,et al.  Fuzzy c-means clustering with spatial information for image segmentation , 2006, Comput. Medical Imaging Graph..

[39]  Luc Van Gool,et al.  SEEDS: Superpixels Extracted Via Energy-Driven Sampling , 2012, International Journal of Computer Vision.

[40]  N. Mitianoudis,et al.  Spatial kernel K-harmonic means clustering for multi-spectral image segmentation , 2007 .

[41]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[42]  Rachid Deriche,et al.  Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation , 2002, International Journal of Computer Vision.

[43]  Lili Ju.  CONFORMING CENTROIDAL VORONOI DELAUNAY TRIANGULATION FOR QUALITY MESH GENERATION , 2007 .

[44]  P. Sivakumar,et al.  A REVIEW ON IMAGE SEGMENTATION TECHNIQUES , 2016 .