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Parallel-Sequential Texture Analysis

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Pattern Recognition and Image Analysis (ICAPR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3687))

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Abstract

Color induced texture analysis is explored, using two texture analysis techniques: the co-occurrence matrix and the color correlogram as well as color histograms. Several quantization schemes for six color spaces and the human-based 11 color quantization scheme have been applied. The VisTex texture database was used as test bed. A new color induced texture analysis approach is introduced: the parallel-sequential approach; i.e., the color correlogram combined with the color histogram. This new approach was found to be highly successful (up to 96% correct classification). Moreover, the 11 color quantization scheme performed excellent (94% correct classification) and should, therefore, be incorporated for real-time image analysis. In general, the results emphasize the importance of the use of color for texture analysis and of color as global image feature. Moreover, it illustrates the complementary character of both features.

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References

  1. Genootschap Onze Taal: Onze Taal Taalkalender. Den Haag: SDU (2003)

    Google Scholar 

  2. Palm, C.: Color texture classification by integrative co-occurrence matrices. Pattern Recognition 37, 965–976 (2004)

    Article  Google Scholar 

  3. Drimbarean, A., Whelan, P.F.: Experiments in colour texture analysis. Pattern Recognition Letters 22, 1161–1167 (2001)

    Article  MATH  Google Scholar 

  4. Mäenpää, T., Pietikäinen, M.: Classification with color and texture: jointly or separately? Pattern Recognition 37, 1629–1640 (2004)

    Article  Google Scholar 

  5. Lin, T., Zhang, H.: Automatic video scene extraction by shot grouping. In: Proceedings of the 15th IEEE International Conference on Pattern Recognition, Barcelona, Spain, vol. 4, pp. 39–42 (2000)

    Google Scholar 

  6. Berlin, B., Kay, P.: Basic color terms: Their universals and evolution. University of California Press, Berkeley (1969)

    Google Scholar 

  7. Derefeldt, G., Swartling, T., Berggrund, U., Bodrogi, P.: Cognitive color. Color Research & Application 29, 7–19 (2004)

    Article  Google Scholar 

  8. van den Broek, E.L., Schouten, T.E., Kisters, P.M.F.: Efficient color space segmentation based on human perception (submitted)

    Google Scholar 

  9. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. Transactions on Systems, Man and Cybernetics 3, 610–621 (1973)

    Article  Google Scholar 

  10. Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: Medioni, G., Nevatia, R., Huttenlocher, D., Ponce, J. (eds.) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 762–768 (1997)

    Google Scholar 

  11. van den Broek, E.L., van Rikxoort, E.M.: Evaluation of color representation for texture analysis. In: Verbrugge, R., Taatgen, N., Schomaker, L.R.B. (eds.) Proceedings of the 16th Belgium-Netherlands Artificial Intelligence Conference, pp. 35–42. Groningen, Netherlands (2004)

    Google Scholar 

  12. Massachusetts Institute of Technology: Vision Texture (2005), http://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html (Last accessed on May 20, 2005)

  13. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)

    Article  Google Scholar 

  14. van den Broek, E.L., van Rikxoort, E.M.: Supplement: Complete results of the ICAPR2005 texture baselines (2005), http://www.few.vu.nl/~egon/publications/pdf/ICAPR2005-Supplement.pdf

  15. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1349–1380 (2000)

    Article  Google Scholar 

  16. Sharma, M., Singh, S.: Evaluation of texture methods for image analysis. In: Linggard, R. (ed.) Proceedings of the 7th Australian and New Zealand Intelligent Information Systems Conference, Perth, Western Australia. ARCME, pp. 117–121 (2001)

    Google Scholar 

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van den Broek, E.L., van Rikxoort, E.M. (2005). Parallel-Sequential Texture Analysis. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_59

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  • DOI: https://doi.org/10.1007/11552499_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28833-6

  • Online ISBN: 978-3-540-31999-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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