Abstract
This paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. By combining the discriminating power of our image descriptor and classifier, our system uses small size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz dataset. High classification rates demonstrate the superiority of the proposed method.
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Montoya-Zegarra, J.A., Papa, J.P., Leite, N.J., da Silva Torres, R., Falcão, A.X. (2007). Rotation-Invariant Texture Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_19
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DOI: https://doi.org/10.1007/978-3-540-76856-2_19
Publisher Name: Springer, Berlin, Heidelberg
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