Abstract
In this paper, we consider the problem of modeling complex texture information using undirected probabilistic graphical models. Texture is a special type of data that one can better understand by considering its local structure. For that purpose, we propose a convolutional variant of the Gaussian gated Boltzmann machine (GGBM) [12], inspired by the co-occurrence matrix in traditional texture analysis. We also link the proposed model to a much simpler Gaussian restricted Boltzmann machine where convolutional features are computed as a preprocessing step. The usefulness of the model is illustrated in texture classification and reconstruction experiments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Caputo, B., Hayman, E., Mallikarjuna, P.: Class-Specific Material Categorisation. In: Int. Conf. on Computer Vision, pp. 1597–1604 (2005)
Cho, K., Raiko, T., Ilin, A.: Gaussian-Bernoulli Deep Boltzmann Machine. In: NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning (2011)
Cho, K., Ilin, A., Raiko, T.: Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines. In: Int. Conf. on Artifical Neural Networks, pp. 10–17 (2011)
Cireşan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Deep, Big, Simple Neural Nets for Handwritten Digit Recognition. Neural Comput. 22(12) (2010)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A Library for Large Linear Classification. JMLR, 1871–1874 (2008)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Trans. Syst., Man, Cybern. 3(6), 610–621 (1973)
Hinton, G., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Hinton, G., Salakhutdinov, R.: Discovering Binary Codes for Documents by Learning Deep Generative Models. Topics in Cognitive Science 3(1), 74–91 (2010)
Kivinen, J., Williams, C.: Multiple Texture Boltzmann Machines. JMLR W&CP 22, 638–646 (2012)
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Int. Conf. Machine Learning, p. 77 (2009)
Liu, L., Fieguth, P.: Texture Classification from Random Features. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 574–586 (2012)
Memisevic, R., Hinton, G.E.: Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines. Neural Comput. 22(6), 1473–1492 (2010)
Ranzato, M., Krizhevsky, A., Hinton, G.E.: Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images. JMLR W&CP 9, 621–628 (2010)
Varma, M., Zisserman, A.: A Statistical Approach to Material Classification Using Image Patch Exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2032–2047 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hao, T., Raiko, T., Ilin, A., Karhunen, J. (2012). Gated Boltzmann Machine in Texture Modeling. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-33266-1_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33265-4
Online ISBN: 978-3-642-33266-1
eBook Packages: Computer ScienceComputer Science (R0)