Diverse fuzzy c-means for image clustering

Abstract Image clustering is a key technique for better accomplishing image annotation and searching in large image repositories. Fuzzy c-means and its variations have achieved excellent performance on image clustering because they allow each image to belong to more than one cluster. However, these methods neglect the relations between different image clusters, and hence often suffer from the “cluster one-sidedness” problem that redundant centers are learned to characterize the same or similar image clusters. To this issue, we propose a diverse fuzzy c-means for image clustering via introducing a novel diversity regularization into the traditional fuzzy c-means objective. This diversity regularization guarantees the learned image cluster centers to be different from each other and to fill the image data space as much as possible. An efficient optimization algorithm is exploited to address the diverse fuzzy c-means objective, which is proved to converge to local optimal solutions and has a satisfactory time complexity. Experiments on synthetic and six image datasets demonstrate the effectiveness of the proposed method as well as the necessity of the diversity regularization.

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

[2]  Zahir Tari,et al.  A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis , 2014, IEEE Transactions on Emerging Topics in Computing.

[3]  Shiri Gordon,et al.  Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Pengtao Xie,et al.  Diversifying Restricted Boltzmann Machine for Document Modeling , 2015, KDD.

[5]  Lingfeng Wang,et al.  Deep Adaptive Image Clustering , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[6]  Miin-Shen Yang,et al.  Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters , 2017, Pattern Recognit..

[7]  Xiaojun Chang,et al.  Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection , 2017, IEEE Transactions on Image Processing.

[8]  Pengtao Xie.  Learning Compact and Effective Distance Metrics with Diversity Regularization , 2015, ECML/PKDD.

[9]  Lei Guo,et al.  Optimal control for networked control systems with disturbances: a delta operator approach , 2017 .

[10]  Di Guo,et al.  Weakly Paired Multimodal Fusion for Object Recognition , 2018, IEEE Transactions on Automation Science and Engineering.

[11]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[12]  Richard J. Hathaway,et al.  Density-Weighted Fuzzy c-Means Clustering , 2009, IEEE Transactions on Fuzzy Systems.

[13]  Fuchun Sun,et al.  Hierarchical Structured Sparse Representation for T–S Fuzzy Systems Identification , 2013, IEEE Transactions on Fuzzy Systems.

[14]  Xinlei Chen,et al.  Large Scale Spectral Clustering Via Landmark-Based Sparse Representation , 2015, IEEE Transactions on Cybernetics.

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

[16]  Jonathan J. Hull,et al.  A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Yi Yang,et al.  Robust Top-k Multiclass SVM for Visual Category Recognition , 2017, KDD.

[18]  Zhongdong Wu,et al.  Fuzzy C-means clustering algorithm based on kernel method , 2003, Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003.

[19]  Songcan Chen,et al.  Fuzzy c-Means Revisited: Towards a Cluster-Center-Free Reformulation , 2010, 2010 Chinese Conference on Pattern Recognition (CCPR).

[20]  Korris Fu-Lai Chung,et al.  Generalized Fuzzy C-Means Clustering Algorithm With Improved Fuzzy Partitions , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Mor Naaman,et al.  Generating diverse and representative image search results for landmarks , 2008, WWW.

[22]  Fuchun Sun,et al.  Joint Block Structure Sparse Representation for Multi-Input–Multi-Output (MIMO) T–S Fuzzy System Identification , 2014, IEEE Transactions on Fuzzy Systems.

[23]  Yi Yang,et al.  Image Clustering Using Local Discriminant Models and Global Integration , 2010, IEEE Transactions on Image Processing.

[24]  Xiaojun Chang,et al.  Semisupervised Feature Analysis by Mining Correlations Among Multiple Tasks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[25]  Muriel Visani,et al.  A new interactive semi-supervised clustering model for large image database indexing , 2014, Pattern Recognit. Lett..

[26]  Aruna Bhat,et al.  POSSIBILITY FUZZY C-MEANS CLUSTERING FOR EXPRESSION INVARIANT FACE RECOGNITION , 2014 .

[27]  Fuchun Sun,et al.  Multimodal Measurements Fusion for Surface Material Categorization , 2018, IEEE Transactions on Instrumentation and Measurement.

[28]  Witold Pedrycz,et al.  Fuzzy C-Means clustering based on dual expression between cluster prototypes and reconstructed data , 2017, Int. J. Approx. Reason..

[29]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[30]  Brendan J. Frey,et al.  Non-metric affinity propagation for unsupervised image categorization , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[31]  Jian-Ping Mei,et al.  Stochastic gradient descent based fuzzy clustering for large data , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[32]  Yi Yang,et al.  Bi-Level Semantic Representation Analysis for Multimedia Event Detection , 2017, IEEE Transactions on Cybernetics.

[33]  Zhihui Li,et al.  Beyond Trace Ratio: Weighted Harmonic Mean of Trace Ratios for Multiclass Discriminant Analysis , 2017, IEEE Transactions on Knowledge and Data Engineering.

[34]  Nenghai Yu,et al.  Annotating personal albums via web mining , 2008, ACM Multimedia.

[35]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Chengqi Zhang,et al.  Convex Sparse PCA for Unsupervised Feature Learning , 2014, ACM Trans. Knowl. Discov. Data.

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

[38]  Feiping Nie,et al.  Compound Rank- $k$ Projections for Bilinear Analysis , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[39]  Michael J. Lyons,et al.  Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[40]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).