A Survey on Clustering Algorithms used to Perform Image Segmentation

: The goal of this survey is to use different Clustering techniques to perform image segmentation. Clustering means a grouping of images which share some common attributes. The purpose of clustering is to get a meaningful result, effective storage, and fast retrieval in various areas. The clustering methods are mainly divided into hierarchical, partitioning, density-based, model-based, grid-based, and soft-computing methods. The goal of this survey is to provide a comprehensive review of different clustering techniques. There are a number of clustering algorithms proposed to perform image segmentation. One needs to choose the best algorithm among them by analyzing the nature of the input image in order to get optimal results.

[1]  Christos Faloutsos,et al.  MindReader: Querying Databases Through Multiple Examples , 1998, VLDB.

[2]  N. Mathur,et al.  The K-means Clustering Based Fuzzy Edge Detection Technique on MRI Images , 2015, 2015 Fifth International Conference on Advances in Computing and Communications (ICACC).

[3]  Sharad Mehrotra,et al.  Query reformulation for content based multimedia retrieval in MARS , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.

[4]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[5]  Wang Luo.  Comparison for Edge Detection of Colony Images , 2006 .

[6]  Frank Chung-Hoon Rhee,et al.  Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-Means , 2007, IEEE Transactions on Fuzzy Systems.

[7]  Matti Pietikäinen,et al.  Computer Vision Using Local Binary Patterns , 2011, Computational Imaging and Vision.

[8]  Yixin Chen,et al.  Content-based image retrieval by clustering , 2003, MIR '03.

[9]  Christos Faloutsos,et al.  FALCON: Feedback Adaptive Loop for Content-Based Retrieval , 2000, VLDB.

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

[11]  Noel E. O'Connor,et al.  A comparative evaluation of interactive segmentation algorithms , 2010, Pattern Recognit..

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

[13]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[14]  Michael R. Lyu,et al.  A novel log-based relevance feedback technique in content-based image retrieval , 2004, MULTIMEDIA '04.

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

[16]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[17]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[18]  Imen Karoui,et al.  Variational Region-Based Segmentation Using Multiple Texture Statistics , 2010, IEEE Transactions on Image Processing.

[19]  Gerard Salton,et al.  Improving retrieval performance by relevance feedback , 1997, J. Am. Soc. Inf. Sci..

[20]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[21]  Kaizhu Huang,et al.  Biased support vector machine for relevance feedback in image retrieval , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

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

[23]  Michael R. Lyu,et al.  Group-based relevance feedback with support vector machine ensembles , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[24]  Hamid R. Tizhoosh,et al.  Evolving fuzzy image segmentation , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[25]  James C. Bezdek,et al.  On cluster validity for the fuzzy c-means model , 1995, IEEE Trans. Fuzzy Syst..

[26]  Shraddha Gangwar,et al.  Survey of Clustering Techniques Enhancing Image Segmentation Process , 2015, 2015 Second International Conference on Advances in Computing and Communication Engineering.

[27]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Shital A. Raut,et al.  Image Segmentation – A State-Of-Art Survey for Prediction , 2009, 2009 International Conference on Advanced Computer Control.

[29]  Xiangyang Wang,et al.  Robust image retrieval based on color histogram of local feature regions , 2010, Multimedia Tools and Applications.

[30]  Giovanni Mettivier,et al.  Digital Imaging Processing , 2010 .

[31]  N. Isa.  Automated Edge Detection Technique for Pap Smear Images Using Moving K-Means Clustering and Modified Seed Based Region Growing Algorithm , 2005 .

[32]  W. Bruce Croft,et al.  Query expansion using local and global document analysis , 1996, SIGIR '96.

[33]  Luigi Cinque,et al.  A clustering fuzzy approach for image segmentation , 2004, Pattern Recognit..

[34]  McGuinnessKevin,et al.  A comparative evaluation of interactive segmentation algorithms , 2010 .

[35]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[36]  Kenneth D. Bailey,et al.  Numerical Taxonomy and Cluster Analysis , 1994 .

[37]  Paul S. Bradley,et al.  Refining Initial Points for K-Means Clustering , 1998, ICML.

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

[39]  Santosh S. Vempala,et al.  On clusterings-good, bad and spectral , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[40]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[41]  Stefan Holban,et al.  Hierarchical clustering techniques and classification applied in Content Based Image Retrieval (CBIR) , 2015, 2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics.

[42]  Qi Tian,et al.  Incorporate support vector machines to content-based image retrieval with relevance feedback , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[43]  Zuwena Musoromy,et al.  Edge detection comparison for license plate detection , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[44]  Indrajit Chakrabarti,et al.  Flooding-based watershed algorithm and its prototype hardware architecture , 2004 .

[45]  Wei-Ying Ma,et al.  Probabilistic query expansion using query logs , 2002, WWW '02.

[46]  Naphtali Rishe,et al.  Content-based image retrieval , 1995, Multimedia Tools and Applications.

[47]  Peter G. Anick.  Using terminological feedback for web search refinement: a log-based study , 2003, SIGIR.