Image clustering using local discriminant models and global integration

Yang, Yi, Xu, Dong, Nie, Feiping, Yan, Shuicheng and Zhuang, YueTing (2010) Image clustering using local discriminant models and global integration. IEEE Transactions on Image Processing, 19 10: 2761-2773. doi:10.1109/TIP.2010.2049235

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Author Yang, Yi
Xu, Dong
Nie, Feiping
Yan, Shuicheng
Zhuang, YueTing
Title Image clustering using local discriminant models and global integration
Journal name IEEE Transactions on Image Processing   Check publisher's open access policy
ISSN 1057-7149
Publication date 2010-10
Sub-type Article (original research)
DOI 10.1109/TIP.2010.2049235
Volume 19
Issue 10
Start page 2761
End page 2773
Total pages 13
Place of publication Piscataway, NJ, United States
Publisher I E E E
Language eng
Abstract In this paper, we propose a new image clustering algorithm, referred to as clustering using local discriminant models and global integration (LDMGI). To deal with the data points sampled from a nonlinear manifold, for each data point, we construct a local clique comprising this data point and its neighboring data points. Inspired by the Fisher criterion, we use a local discriminant model for each local clique to evaluate the clustering performance of samples within the local clique. To obtain the clustering result, we further propose a unified objective function to globally integrate the local models of all the local cliques. With the unified objective function, spectral relaxation and spectral rotation are used to obtain the binary cluster indicator matrix for all the samples.We show that LDMGI shares a similar objective function with the spectral clustering (SC) algorithms, e.g., normalized cut (NCut). In contrast to NCut in which the Laplacian matrix is directly calculated based upon a Gaussian function, a new Laplacian matrix is learnt in LDMGI by exploiting both manifold structure and local discriminant information. We also prove that K-means and discriminative K-means (DisKmeans) are both special cases of LDMGI. Extensive experiments on several benchmark image datasets demonstrate the effectiveness of LDMGI. We observe in the experiments that LDMGI is more robust to algorithmic parameter, when compared with NCut. Thus, LDMGI is more appealing for the real image clustering applications in which the ground truth is generally not available for tuning algorithmic parameters.
Keyword Clustering
K-means clustering
Local discriminant model
Spectral clustering
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
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School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 72 times in Thomson Reuters Web of Science Article | Citations
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Created: Mon, 05 Mar 2012, 15:09:32 EST by Mr Mathew Carter on behalf of School of Information Technol and Elec Engineering