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.
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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
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DOI: https://doi.org/10.1007/978-3-642-33266-1_16
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