Abstract
Inspired by the efficient coding hypothesis and simple-to-complex cell hierarchy of the visual system, we study a universal visual dictionary learned from natural scenes using sparse coding for recognition. The vocabularies are similar to V1 simple cells receptive fields. Max pooling is done in a local region (”block”) so that the features are translation invariant, which is the function of complex cells. Macro-features of a grid of overlapping spatial blocks are built and fed to a linear SVM classifier for recognition. We have tested the learned universal visual dictionary on different recognition tasks and demonstrated the effectiveness of the model.
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
Serre, T., Wolf, L., Poggio, T.: Object recognition with features inspired by visual cortex. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 994–1000 (2005)
Bengio, Y.: Learning deep architectures for ai. Foundations and Trends® in Machine Learning 2, 1–127 (2009)
Olshausen, B.A., et al.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)
Labusch, K., Barth, E., Martinetz, T.: Simple method for high-performance digit recognition based on sparse coding. IEEE Transactions on Neural Networks 19, 1985–1989 (2008)
Raina, R., Battle, A., Lee, H., Packer, B., Ng, A.Y.: Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine Learning, pp. 759–766 (2007)
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Supervised dictionary learning. arXiv preprint arXiv:0809.3083 (2008)
Huang, K., Aviyente, S.: Sparse representation for signal classification. In: Advances in Neural Information Processing Systems (NIPS), pp. 609–616 (2006)
Bradley, D.M., Bagnell, J.A.: Differential sparse coding. In: Advances in Neural Information Processing Systems (NIPS) (2008)
Yang, J., Yu, K., Huang, T.: Supervised translation-invariant sparse coding. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3517–3524 (2010)
Mairal, J., Bach, F., Ponce, J.: Task-driven dictionary learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 791–804 (2012)
Shan, H., Cottrell, G.W.: Looking around the backyard helps to recognize faces and digits. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)
Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649 (2012)
Sim, T., Baker, S., Bsat, M.: The cmu pose, illumination, and expression (pie) database. In: IEEE Conference on Automatic Face and Gesture Recognition, pp. 46–51 (2002)
Cai, D., He, X., Han, J.: Semi-supervised discriminant analysis. In: International Conference on Computer Vision (ICCV), pp. 1–7 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ding, L., Xu, J. (2013). A Universal Visual Dictionary Learned from Natural Scenes for Recognition. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-42051-1_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
eBook Packages: Computer ScienceComputer Science (R0)