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Classifying image texture with statistical landscape features

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Abstract

This paper proposes to use three-dimensional information derived from the graph of an image function for texture description. The graph of an image function is a rumpled surface appearing like a landscape. To characterize the texture through this landscape, six novel texture feature curves based on the statistics of the geometrical and topological properties of the solids shaped by the graph and a variable horizontal plane are used. The proposed statistical landscape features have been shown by systematic experiments to offer very low error rates on a large subset of the Brodatz texture album having excluded some nonhomogeneous images, the entire Brodatz texture set, as well as the VisTex texture collection.

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Acknowledgements

The research work presented in this paper is supported by National Natural Science Foundation of China, Grant No. 60275010, and Science and Technology Commission of Shanghai Municipality, Grant No. 04JC14014.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Information Science and Engineering, Fudan University, Shanghai, 200433, China

    Cun Lu Xu & Yan Qiu Chen

Authors
  1. Cun Lu Xu
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  2. Yan Qiu Chen
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Correspondence to Yan Qiu Chen.

Appendix

Appendix

Fig. 7
figure 7

Brodatz texture set. From left to right, top to bottom: D1, D2, ..., D112

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Fig. 8
figure 8

Some nonhomogeneous images in the Brodatz texture set. From left to right: D7, D42, D43, D44, D45, D58, D59, D62, D69, D72, D90 and D91

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Table 4 Detailed classification error listing for kNN (k=1) of different methods in Table 2
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Xu, C.L., Chen, Y.Q. Classifying image texture with statistical landscape features. Pattern Anal Applic 8, 321–331 (2006). https://doi.org/10.1007/s10044-005-0014-6

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