Abstract
In computer vision, texture plays an important role. In this work we propose five human perceptual texture features heuristically extracted. Since a modeling can not be obtained from these features, we use a discriminant analysis technique to examine the discriminant power of each texture descriptor in order to select the most relevant. This task is done by a stepwise inclusion of variables indicating, furthermore, whether all of them are valuable and necessary producing a set of optimal discriminant variables. These selection procedures have been tested using Brodatz texture images, a benchmark in texture analysis.
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© 1998 Springer-Verlag Berlin Heidelberg
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Grau, A., Aranda, J., Climent, J. (1998). Stepwise selection of perceptual texture features. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033309
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DOI: https://doi.org/10.1007/BFb0033309
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