A comparative study of texture measures with classification based on feature distributions

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Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)




This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently. For classification a method based on Kullback discrimination of sample and prototype distributions is used. The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented

Extended Abstract


title = "A comparative study of texture measures with classification based on featured distributions",
journal = "Pattern Recognition",
volume = "29",
number = "1",
pages = "51 - 59",
year = "1996",
note = "",
issn = "0031-3203",
doi = "http://dx.doi.org/10.1016/0031-3203(95)00067-4",
url = "http://www.sciencedirect.com/science/article/pii/0031320395000674 http://de.evo-art.org/index.php?title=A_comparative_study_of_texture_measures_with_classification_based_on_feature_distributions",
author = "Timo Ojala and Matti Pietikäinen and David Harwood",
keywords = "Texture analysis",
keywords = "Classification",
keywords = "Feature distribution",
keywords = "Brodatz textures",
keywords = "Kullback discriminant",
keywords = "Performance evaluation"

Used References

1. L. Van Gool, P. Dewaele and A. Oosterlinck, Texture analysis anno 1983, Comput. Vis. Graphics Image Process. 29(3), 336-357 (1985). 2. R.M. Haralick and L. Shapiro, Computer and Robot Vision. Vol. 1. Addison-Wesley, New York (1992).

3. J. Weszka, C. Dyer and A. Rosenfeld, A comparative study of texture measures for terrain classification, IEEE Trans. Syst. Man. C ybernet. SMC-6, 269-285'(1976).

4. J. M. H. Du Buf, M. Kardan and M. Spann, Texture feature performance for image segmentation, Pattern Recognition 23(3/4), 291-309 (1990).

5. P. P. Ohanian and R. C. Dubes, Performance evaluation for four classes of textural features, Pattern Recognition 25, 819-833 (1992).

6. D. Harwood, T. Ojala, M. Pietiktiinen, S. Kelman and L. S. Davis, Texture classification by center-symmetric auto-correlation, using Kullback discrimination of distributions, Pattern Recognition Lett. 16(1), 1-10 (1995).

7. M. Unser, Sum and difference histograms for texture classification, IEEE Trans. Pattern Anal. Mack lntell. 8(1), 118-125 (1986).

8. K.I. Laws, Textured image segmentation, Report 940, Image Processing Institute, Univ. of Southern California (1980).

9. L. Wang and D. C. He, Texture classification using texture spectrum, Pattern Recognition 23, 905-910 (1990).

10. H. C. Shen and C. Y. C. Bie, Feature frequency matrices as texture image representation, Pattern Recoonition Lett. 13(3), 195-205 (1992).

11. S. Kullback, Information Theory and Statistics, Dover Publications, New York (1968).

12. R.R. Sokal and F. J. Rohlf, Biometry. W. H. Freeman and Co (1969).

13. P. Brodatz, Textures: A Photographic Album for Artists and Designers. Dover Publications, New York (1966).

14. M. Pietik/iinen, A. Rosenfeld and L.S. Davis, Experiments with texture classification using averages of local pattern matches, IEEE Trans. Syst. Man Cybern. SMC-13(3), 421-426 (1983).

15. M. Pietik/iinen, T. Ojala, J. Nisula and J. Heikkinen, Experiments with two industrial problems using texture classification based on feature distribution, SPIE Vol. 2354 Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection and Active Vision. 197-204. Boston, Massachussetts (1994).


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