Abstract
This paper presents a novel algorithm to build hierarchies from independent component analyzer mixtures and its application to image similarity measure. The hierarchy algorithm composes an agglomerative (bottom-up) clustering from the estimated parameters (basis vectors and bias terms) of the ICA mixture. Merging at different levels of the hierarchy is made using the Kullback-Leibler distance between clusters. The procedure is applied to merge similar patches on a natural image, to group different images of an object, and to create hierarchical levels of clustering from images of different objects. Results show suitable image hierarchies obtained by clustering from basis functions to higher-level structures.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lee, T.W., Lewicki, M.S., Sejnowski, T.J.: ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation. IEEE Trans. on Patt. Analysis and Machine Intelligence 22(10), 1078–1089 (2000)
Penny, W.D., Roberts, S.: Mixtures of independent component analyzers. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 527–534. Springer, Heidelberg (2001)
Choudrey, R., Roberts, S.: Variational Mixture of Bayesian Independent Component Analysers. Neural Computation 15(1), 213–252 (2003)
Mollah, N.H., Minami, M., Eguchi, S.: Exploring Latent Structure of Mixture ICA Models by the Minimum ß-Divergence Method. Neural Computation 18(1), 166–190 (2006)
Bell, A.J., Sejnowski, T.J.: The Independent Components of natural scenes are edge filters. Vision Research 37(23), 3327–3338 (1997)
Van Hateren, J.H., van der Shaaf, A.: Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of Royal Society of London: B 265, 359–366 (1998)
Matsuda, Y., Yamaguchi, K.: Linear multilayer ICA generating hierarchical edge detectors. Neural Computation 19(1), 218–230 (2007)
Lee, T.S., Mumford, D.: Hierarchical Bayesian inference in the visual cortex. Journal of the Optical Society of America A 20(7), 1434–1448 (2003)
Mackay, D.J.: Information theory, inference, and learning algorithms. Cambridge University Press, Cambridge (2004)
Vergara, L., Salazar, A., Igual, J., Serrano, A.: Data Clustering Methods Based on Mixture of Independent Component Analyzers. In: Proc. of ICA Research Network International Workshop, ICArn, Liverpool, pp. 127–130 (2006)
Hyvarinen, A., Hoyer, P.O., Inki, M.: Topographic independent component analysis. Neural Computation 13(7), 1527–1558 (2001)
Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library (COIL-100), Technical Report CUCS-006-96 (February 1996)
Salazar, A., Unió, J.M., Serrano, A., Gosalbez, J.: Neural networks for defect detection in non-destructive evaluation by sonic signals. LNCS, vol. 4507, pp. 631–638. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Serrano, A., Salazar, A., Igual, J., Vergara, L. (2007). Image Similarity Based on Hierarchies of ICA Mixtures. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_98
Download citation
DOI: https://doi.org/10.1007/978-3-540-74494-8_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74493-1
Online ISBN: 978-3-540-74494-8
eBook Packages: Computer ScienceComputer Science (R0)