A novel clustering-based image segmentation via density peaks algorithm with mid-level feature

Image segmentation is an important and fundamental task in computer vision. Its performance is mainly influenced by feature representations and segmentation algorithms. In this paper, we propose a novel clustering-based image segmentation approach which can be called ICDP algorithm. It is able to capture the inherent structure of image and detect the nonspherical clusters. Compared to the other segmentation methods based on clustering, there are several advantages as follows: (1) Integral channel features are used to clearly and comprehensively represent the input image by naturally integrating heterogeneous sources of information; (2) cluster number can be determined directly and cluster centers are able to be identified automatically; (3) hierarchical segmentation is easy to be achieved via ICDP algorithm. The PSNR and MSE are applied to quantitatively evaluate the segmentation performance. Experimental results clearly demonstrate the effectiveness of our novel image segmentation algorithm.

[1]  L. Pacheco,et al.  Improving Clustering Algorithms for Image Segmentation using Contour and Region Information , 2006, 2006 IEEE International Conference on Automation, Quality and Testing, Robotics.

[2]  Montse Pardàs,et al.  Deleted DOI: Audiovisual Head Orientation Estimation with Particle Filtering in Multisensor Scenarios , 2008 .

[3]  Fatih Murat Porikli,et al.  Integral histogram: a fast way to extract histograms in Cartesian spaces , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  A. Khurshid.  An Approach to Image Segmentation using K-means Clustering Algorithm , 2012 .

[5]  Ivan Laptev,et al.  Improvements of Object Detection Using Boosted Histograms , 2006, BMVC.

[6]  Nor Ashidi Mat Isa,et al.  Color image segmentation using adaptive unsupervised clustering approach , 2013, Appl. Soft Comput..

[7]  Andrew Chi-Sing Leung,et al.  PSO-based K-Means clustering with enhanced cluster matching for gene expression data , 2012, Neural Computing and Applications.

[8]  Guojun Dai,et al.  Robust and smart spectral clustering from normalized cut , 2012, Neural Computing and Applications.

[9]  Zhen Ma,et al.  A review of algorithms for medical image segmentation and their applications to the female pelvic cavity , 2010, Computer methods in biomechanics and biomedical engineering.

[10]  V. Kavitha,et al.  Color Image Segmentation using ERKFCM , 2012 .

[11]  Zhuowen Tu,et al.  Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

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

[13]  Fatih Murat Porikli,et al.  Human Detection via Classification on Riemannian Manifolds , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[15]  James C. Tilton,et al.  Image segmentation by region growing and spectral clustering with a natural convergence criterion , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[16]  Hai Jin,et al.  Color Image Segmentation Based on Mean Shift and Normalized Cuts , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  Md Rafiqul Islam,et al.  Segmentation of color image using adaptive thresholding and masking with watershed algorithm , 2013, 2013 International Conference on Informatics, Electronics and Vision (ICIEV).

[18]  Qiang Chen,et al.  Fuzzy c-means clustering with weighted image patch for image segmentation , 2012, Appl. Soft Comput..

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

[20]  Marcus A. Magnor,et al.  An Approach Toward Fast Gradient-Based Image Segmentation , 2015, IEEE Transactions on Image Processing.

[21]  Jitendra Malik,et al.  Local figure-ground cues are valid for natural images. , 2007, Journal of vision.

[22]  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.

[23]  Xiaohao Cai,et al.  Variational image segmentation model coupled with image restoration achievements , 2014, Pattern Recognit..

[24]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[25]  Hongjie Jia,et al.  Research of semi-supervised spectral clustering algorithm based on pairwise constraints , 2012, Neural Computing and Applications.

[26]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[27]  Navid Razmjooy,et al.  Color image segmentation using neuro-fuzzy system in a novel optimized color space , 2012, Neural Computing and Applications.

[28]  Mei-Chen Yeh,et al.  Fast Human Detection Using a Cascade of Histograms of Oriented Gradients , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[29]  Shohreh Kasaei,et al.  Fuzzy Image Segmentation Using Membership Connectedness , 2008, EURASIP J. Adv. Signal Process..

[30]  Joseph J. Lim,et al.  Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Pietro Perona,et al.  Integral Channel Features , 2009, BMVC.

[32]  A. Immanuel Selvakumar,et al.  Distance metric-based time-efficient fuzzy algorithm for abnormal magnetic resonance brain image segmentation , 2011, Neural Computing and Applications.

[33]  Aboul Ella Hassanien,et al.  Adaptive k-means clustering algorithm for MR breast image segmentation , 2013, Neural Computing and Applications.

[34]  Sumit K. Nath,et al.  Fast Graph Partitioning Active Contours for Image Segmentation Using Histograms , 2009, EURASIP J. Image Video Process..

[35]  Soni Chaturvedi,et al.  An Approach to Image Segmentation using K-means Clustering Algorithm , 2012 .

[36]  Qing Li,et al.  Spatial Homogeneity-Based Fuzzy c-Means Algorithm for Image Segmentation , 2005, FSKD.

[37]  Fan Meng,et al.  Image Segmentation via Improving Clustering Algorithms with Density and Distance , 2015, ITQM.

[38]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[39]  Zhuowen Tu,et al.  Feature Mining for Image Classification , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  Sean Hughes,et al.  Clustering by Fast Search and Find of Density Peaks , 2016 .

[41]  Wenjian Wang,et al.  Principal pixel analysis and SVM for automatic image segmentation , 2013, Neural Computing and Applications.

[42]  Oscar C. Au,et al.  An adaptive unsupervised approach toward pixel clustering and color image segmentation , 2010, Pattern Recognit..