Abstract
Clustering, or grouping samples which share similar features, is a recurrent problem in computer vision and pattern recognition. The core element of a clustering algorithm is the similarity measure. In this regard information theory offers a wide range of measures (not always metrics) which inspire clustering algorithms through their optimization. In addition, information theory also provides both theoretical frameworks and principles to formulate the clustering problem and provide effective algorithms. Clustering is closely related to the segmentation problem, already presented in Chapter 3. In both problems, finding the optimal number of clusters or regions is a challenging task. In the present chapter we cover this question in depth. To that end we explore several criteria for model order selection.
All the latter concepts are developed through the description and discussion of several information theoretic clustering algorithms: Gaussian mixtures, Information Bottleneck, Robust Information Clustering (RIC) and IT-based Mean Shift. At the end of the chapter we also discuss basic strategies to form clustering ensembles.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Key References
A. Peñalver, F. Escolano, and J.M. Sáez. “EBEM: An Entropy-Based EM Algorithm for Gaussian Mixture Models”. International Conference on Pattern Recognition, Hong Kong (China) (2006)
A. Peñalver, F. Escolano, and J.M. Sáez. “Two Entropy-Based Methods for Learning Unsupervised Gaussian Mixture Models”. SSPR/SPR — LNCS (2006)
M. Figueiredo and A.K. Jain. “Unsupervised Learning of Finite Mixture Models”. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3): 381–396 (2002)
N. Srebro, G. Shakhnarovich, and S. Roweis. “An Investigation of Computational and Informational Limits in Gaussian Mixture Clustering”. International Conference on Machine Learning (2006)
N.Z. Tishby, F. Pereira, and W. Bialek. “The Information Bottleneck method”. 37th Allerton Conference on Communication, Control and Computing (1999)
N. Slonim, N. Friedman, and N. Tishby. “Multivariate Information Bottleneck”. Neural Computation 18: 1739–1789 (2006)
J. Goldberger, S. Gordon, and H. Greenspan. “Unsupervised Image-Set Clustering Using an Information Theoretic Framework”. IEEE Transactions on Image Processing 15(2): 449–458 (2006)
N. Slonim and N. Tishby. “Agglomerative Information Bottleneck”. In Proceeding of Neural Information Processing Systems (1999)
W. Punch, A. Topchy, and A. Jain. “Clustering Ensembles: Models of Consensus and Weak Partitions”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(12): 1866–1881 (2005)
Rights and permissions
Copyright information
© 2009 Springer Verlag London Limited
About this chapter
Cite this chapter
(2009). Image and Pattern Clustering. In: Information Theory in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-297-9_5
Download citation
DOI: https://doi.org/10.1007/978-1-84882-297-9_5
Publisher Name: Springer, London
Print ISBN: 978-1-84882-296-2
Online ISBN: 978-1-84882-297-9
eBook Packages: Computer ScienceComputer Science (R0)