Abstract
We propose an extension of the Restricted Boltzmann Machine (RBM) that allows the joint shape and appearance of foreground objects in cluttered images to be modeled independently of the background. We present a learning scheme that learns this representation directly from cluttered images with only very weak supervision. The model generates plausible samples and performs foreground-background segmentation. We demonstrate that representing foreground objects independently of the background can be beneficial in recognition tasks.
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References
Hinton, G.E.: Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation 14(8), 1771–1800 (2002)
Hinton, G.E., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Computation 18, 1527–1554 (2006)
Lee, H., Gross, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: ICML (2009)
Le Roux, N., Heess, N., Shotton, J., Winn, J.: Learning a Generative Model of Images by Factoring Appearance and Shape. Neural Computation 23(3), 593–650 (2011)
Huang, G.B., Rames, M., Berg, T., Learned-Miller, E.: Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. TR 07-49; Univ. of Mass., Amherst (2007)
Ranzato, M., Hinton, G.: Modeling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines. In: CVPR (2010)
Salakhutdinov, R., Hinton, G.E.: Deep Boltzmann Machines. In: AISTATS (2009)
Tang, Y.: Gated Boltzmann Machine for Recognition under Occlusion. In: NIPS Workshop on Transfer Learning by Learning Rich Generative Models (2010)
Tieleman, T.: Training Restricted Boltzmann Machines using Approximations to the Likelihood Gradient. In: ICML (2008)
Wang, J.Y.A., Adelson, E.H.: Representing moving images with layers. IEEE Transactions on Image Processing 3(5), 625 (1994)
Williams, C.K.I., Titsias, M.K.: Greedy Learning of Multiple Objects in Images using Robust Statistics and Factorial Learning. Neural Comp. 16(5), 1039–1062 (2004)
Wolf, L., Hassner, T., Taigman, Y.: Similarity scores based on background samples. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009. LNCS, vol. 5995, pp. 88–97. Springer, Heidelberg (2010)
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Heess, N., Le Roux, N., Winn, J. (2011). Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_2
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DOI: https://doi.org/10.1007/978-3-642-21738-8_2
Publisher Name: Springer, Berlin, Heidelberg
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