Abstract
The shape of an object is an important characteristic for many vision problems such as segmentation, detection and tracking. Being independent of appearance, it is possible to generalize to a large range of objects from only small amounts of data. However, shapes represented as silhouette images are challenging to model due to complicated likelihood functions leading to intractable posteriors. In this paper we present a generative model of shapes which provides a low dimensional latent encoding which importantly resides on a smooth manifold with respect to the silhouette images. The proposed model propagates uncertainty in a principled manner allowing it to learn from small amounts of data and providing predictions with associated uncertainty. We provide experiments that show how our proposed model provides favorable quantitative results compared with the state-of-the-art while simultaneously providing a representation that resides on a low-dimensional interpretable manifold.
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
Similar content being viewed by others
Notes
- 1.
When we show generated silhouettes from any model, we actually show grayscale images denoting pixel-wise probabilities of turning white rather than binary samples.
References
Abadi, M., et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems (2015). Software available from tensorflow.org
Bengio, Y., LeCun, Y., et al.: Scaling learning algorithms towards AI. Large-Scale Kernel Mach. 34(5), 1–41 (2007)
Campbell, N.D.F., Kautz, J.: Learning a manifold of fonts. ACM Trans. Graph. (SIGGRAPH) 33(4), 91 (2014)
Carreira-Perpiñán, M.Á., Hinton, G.E.: On contrastive divergence learning. In: Cowell, R.G., Ghahramani, Z. (eds.) Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, AISTATS 2005, Bridgetown, Barbados, 6–8 January 2005. Society for Artificial Intelligence and Statistics (2005)
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Lee, D.D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain, 5–10 December 2016, pp. 2172–2180 (2016)
Damianou, A.C., Lawrence, N.D.: Deep Gaussian processes. In: Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2013, Scottsdale, AZ, USA, 29 April–1 May 2013. JMLR Workshop and Conference Proceedings, vol. 31, pp. 207–215. JMLR.org (2013)
Davies, R., Twining, C., Taylor, C.: Statistical Models of Shape: Optimisation and Evaluation. Springer, Heidelberg (2008). https://doi.org/10.1007/978-1-84800-138-1
Elhabian, S.Y., Whitaker, R.T.: ShapeOdds: variational Bayesian learning of generative shape models. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21–26 July 2017, pp. 2185–2196. IEEE Computer Society (2017)
Eslami, S.M.A., Williams, C.K.I.: A generative model for parts-based object segmentation. In: Bartlett, P.L., Pereira, F.C.N., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a Meeting Held Lake Tahoe, Nevada, USA, 3–6 December 2012, pp. 100–107 (2012)
Eslami, S.A., Heess, N., Williams, C.K., Winn, J.: The shape Boltzmann machine: a strong model of object shape. Int. J. Comput. Vis. 107(2), 155–176 (2014)
Goodfellow, I.J., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Montreal, Quebec, Canada, 8–13 December 2014, pp. 2672–2680 (2014)
Hinton, G.E.: A practical guide to training restricted Boltzmann machines. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 599–619. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8_32
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2014)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (ICLR) (2013)
Kirillov, A., Gavrikov, M., Lobacheva, E., Osokin, A., Vetrov, D.P.: Deep part-based generative shape model with latent variables. In: Wilson, R.C., Hancock, E.R., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, 19–22 September 2016. BMVA Press (2016)
Lawrence, N.D.: Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J. Mach. Learn. Res. 6, 1783–1816 (2005)
Lawrence, N.D., Candela, J.Q.: Local distance preservation in the GP-LVM through back constraints. In: Cohen, W.W., Moore, A. (eds.) Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, 25–29 June 2006. ACM International Conference Proceeding Series, vol. 148, pp. 513–520. ACM (2006)
Li, F., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2004, Washington, DC, USA, 27 June–2 July 2004, p. 178. IEEE Computer Society (2004)
Li, W., Viola, F., Starck, J., Brostow, G.J., Campbell, N.D.F.: Roto++: accelerating professional rotoscoping using shape manifolds. ACM Trans. Graph. (SIGGRAPH) 35(4), 62 (2016)
Maddison, C.J., Mnih, A., Teh, Y.W.: The concrete distribution: a continuous relaxation of discrete random variables. abs/1611.00712 (2016)
Prisacariu, V.A., Reid, I.D.: PWP3D: real-time segmentation and tracking of 3D objects. Int. J. Comput. Vis. 98(3), 335–354 (2012)
Prisacariu, V.A., Reid, I.D.: Nonlinear shape manifolds as shape priors in level set segmentation and tracking. In: The 24th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, Colorado Springs, CO, USA, 20–25 June 2011, pp. 2185–2192. IEEE Computer Society (2011)
Roy, A., Todorovic, S.: Combining bottom-up, top-down, and smoothness cues for weakly supervised image segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21–26 July 2017, pp. 7282–7291. IEEE Computer Society (2017)
Smolensky, P.: Information processing in dynamical systems: foundations of harmony theory. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. pp. 194–281 (1986)
Snoek, J., Adams, R.P., Larochelle, H.: Nonparametric guidance of autoencoder representations using label information. J. Mach. Learn. Res. 13, 2567–2588 (2012)
Tosi, A., Hauberg, S., Vellido, A., Lawrence, N.D.: Metrics for probabilistic geometries. In: Zhang, N.L., Tian, J. (eds.) Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, UAI 2014, Quebec City, Quebec, Canada, 23–27 July 2014, pp. 800–808. AUAI Press (2014)
Tsogkas, S., Kokkinos, I., Papandreou, G., Vedaldi, A.: Semantic part segmentation with deep learning. arXiv preprint arXiv:1505.02438 (2015)
Turmukhambetov, D., Campbell, N.D.F., Goldman, D.B., Kautz, J.: Interactive sketch-driven image synthesis. Comput. Graph. Forum 34(8), 130–142 (2015)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7–12 June 2015, pp. 1912–1920. IEEE Computer Society (2015)
Acknowledgements
This work was supported by the EPSRC CAMERA (EP/M023281/1) grant and the Royal Society.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Di Martino, A., Bodin, E., Ek, C.H., Campbell, N.D.F. (2019). Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-20870-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20869-1
Online ISBN: 978-3-030-20870-7
eBook Packages: Computer ScienceComputer Science (R0)