Abstract
This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing. At the feature extraction stage, a small set of random features is extracted from local image patches. The random features are embedded into a bag-of-words model to perform texture classification, thus learning and classification are carried out in the compressed domain. The proposed unconventional random feature extraction is simple, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We report extensive experiments comparing the proposed method to the state-of-the-art in texture classification on four databases: CUReT, Brodatz, UIUC and KTH-TIPS. Our approach leads to significant improvements in classification accuracy and reductions in feature dimensionality, exceeding the best reported results on CUReT, Brodatz and KTH-TIPS.
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References
Leung, T., Malik, J.: Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons. International Journal of Computer Vision 43, 29–44 (2001)
Cula, O.G., Dana, K.J.: 3D Texture Recognition Using Bidirectional Feature Histograms. International Journal of Computer Vision 59, 33–60 (2004)
Varma, M., Zisserman, A.: A Statistical Approach to Texture Classification from Single Images. International Journal of Computer Vision 62, 61–81 (2005)
Varma, M., Zisserman, A.: A Statistical Approach to Material Classification Using Image Patches. IEEE Trans. Pattern Analysis and Machine Intelligence 31, 2032–2047 (2009)
Lazebnik, S., Schmid, C., Ponce, J.: A Sparse Texture Representation Using Local Affine Regions. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 1265–1278 (2005)
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study. International Journal of Computer Vision 73, 213–238 (2007)
Candès, E., Tao, T.: Near-Optimal Signal Recovery from Random Projections: Universal Encoding Stratigies? IEEE Trans. Information Theory 52, 5406–5425 (2006)
Donoho, D.L.: Compressed Sensing. IEEE Trans. Information Theory 52, 1289–1306 (2006)
Mellor, M., Hong, B.-W., Brady, M.: Locally Rotation, Contrast, and Scale Invariant Descriptors for Texture Analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 30, 52–61 (2008)
Donoho, D., Tanner, J.: Observed Universality of Phase Transitions in High-Dimensional Geometry, with Implications for Modern Data Analysis and Signal Processing. Phil. Trans. R. Soc. A 367, 4273–4293 (2009)
Romberg, J.: Imaging via Compressive Sampling. IEEE Signal Processing Magazine 25, 14–20 (2008)
Wright, J., Yang, A., Ganesh, A., Sastry, S.S., Ma, Y.: Robust Face Recognition via Sparse Representation. IEEE Trans. Pattern Analysis and Machine Intelligence 31, 210–217 (2009)
Candès, E., Tao, T.: Decoding by Linear Programming. IEEE Trans. Information Theory 51, 4203–4215 (2005)
Graf, S., Luschgy, H.: Foundations of Quantization for Probability Distributions. Springer, New York (2000)
Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Conners, R.W., Harlow, C.A.: A Theoretical Comparison of Texture Algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 2, 204–222 (1980)
Unser, M.: Sum and Difference Histograms for Texture Classification. IEEE Trans. Pattern Analysis and Machine Intelligence 8, 118–125 (1986)
Ojala, T., Valkealahti, K., Oja, E., Pietikäinen, M.: Texture Discrimination with Multidimensional Distributions of Signed Gray-Level Differences. Pattern Recognition 34, 727–739 (2001)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 971–987 (2002)
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications, New York (1966)
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)
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Liu, L., Fieguth, P., Kuang, G. (2011). Compressed Sensing for Robust Texture Classification. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19315-6_30
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DOI: https://doi.org/10.1007/978-3-642-19315-6_30
Publisher Name: Springer, Berlin, Heidelberg
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