Abstract
The recent progress in sparse coding and deep learning has made unsupervised feature learning methods a strong competitor to hand-crafted descriptors. In computer vision, success stories of learned features have been predominantly reported for object recognition tasks. In this paper, we investigate if and how feature learning can be used for material recognition. We propose two strategies to incorporate scale information into the learning procedure resulting in a novel multi-scale coding procedure. Our results show that our learned features for material recognition outperform hand-crafted descriptors on the FMD and the KTH-TIPS2 material classification benchmarks.
Recommended for submission to YRF2014 by Dr. Mario Fritz.
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References
Challenges in learning hierarchical models: Transfer learning and optimization. https://sites.google.com/site/nips2011workshop/transfer-learning-challenge
Pylearn2 vision, a python library for machine learning. http://deeplearning.net/software/pylearn2/
Sparse modeling software, an optimization toolbox for solving various sparse estimation problems. http://spams-devel.gforge.inria.fr/
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: NIPS (2007)
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y.: Theano: a CPU and GPU math expression compiler. In: Proceedings of the Python for Scientific Computing Conference (SciPy) (2010)
Caputo, B., Hayman, E., Mallikarjuna, P.: Class-specific material categorisation. In: ICCV (2005)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1–27 (2011)
Courville, A., Bergstra, J., Bengio, Y.: A spike and slab restricted boltzmann machine. In: JMLR (2011)
Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real-world surfaces. ACM Trans. Graph. 18(1), 1–34 (1999)
Farabet, C., Couprie, C., Najman, L., LeCun., Y.: Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers. In: ICML (2012)
Goodfellow, I., Couville, A., Bengio, Y.: Large-scale feature learning with spike-and-slab sparse coding. In: ICML (2012)
Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the significance of real-world conditions for material classification. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 253–266. Springer, Heidelberg (2004)
Hinton, G., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)
Hu, D., Bo, L., Ren, X.: Toward robust material recognition for everyday objects. In: BMVC (2011)
Hussain, S., Triggs, B.: Visual recognition using local quantized patterns. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 716–729. Springer, Heidelberg (2012)
Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. IJCV 43(1), 29–44 (2001)
Li, W., Fritz, M.: Recognizing materials from virtual examples. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 345–358. Springer, Heidelberg (2012)
Liu, C., Sharan, L., Adelson, E.H., Rosenholtz, R.: Exploring features in a bayesian framework for material recognition. In: CVPR (2010)
Mäenpää, T., Pietikäinen, M.: Multi-scale binary patterns for texture analysis. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 885–892. Springer, Heidelberg (2003)
IEEE Trans. Pattern Anal. Mach. Intell. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. 24(7), 971–987 (2002)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)
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)
Qi, X., Xiao, R., Guo, J., Zhang, L.: Pairwise rotation invariant co-occurrence local binary pattern. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 158–171. Springer, Heidelberg (2012)
Raina, R., Battle, A., Lee, H., Packer, B., Ng, A.: Self-taught learning: transfer learning from unlabeled data. In: ICML (2007)
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)
Varma, M., Zisserman, A.: Classifying images of materials: achieving viewpoint and illumination independence. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 255–271. Springer, Heidelberg (2002)
Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vis. - Special Issue on Texture Anal. Synth. 62(1–2), 61–81 (2005)
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Li, W. (2014). Learning Multi-scale Representations for Material Classification. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_65
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DOI: https://doi.org/10.1007/978-3-319-11752-2_65
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