Abstract
Texture is an important visual attribute used to discriminate images. Although statistical features have been successful, texture descriptors do not capture the richness of details present in the images. In this paper we propose a novel approach for texture analysis based on partial differential equations (PDE) of Perona and Malik. Basically, an input image f is decomposed into two components f = u + v, where u represents the cartoon component and v represents the textural component. We show how this procedure can be employed to enhance the texture attribute. Based on the enhanced texture information, Gabor filters are applied in order to compose a feature vector. Experiments on two benchmark datasets demonstrate the superior performance of our approach with an improvement of almost 6%. The results strongly suggest that the proposed approach can be successfully combined with different methods of texture analysis.
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Machado, B.B., Gonçalves, W.N., Bruno, O.M. (2011). Enhancing the Texture Attribute with Partial Differential Equations: A Case of Study with Gabor Filters. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_31
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DOI: https://doi.org/10.1007/978-3-642-23687-7_31
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