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A Method of the Extraction of Texture Feature

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Advances in Computation and Intelligence (ISICA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5370))

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Abstract

In order to understand the emotional information of the color image, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap’ between the visual features and the richness of human perception. In this paper, we firstly get the ROI using the Eye tracker and divide every image into two regions including Regions of Interest (ROI) and Non- Regions of Interest (Non-ROI). Secondly, we use the analytical hierarchy process (AHP) to provide a systematical way to evaluate the fit weights of ROI and Non-ROI. Finally, using the improved GLCM, we extract the texture feature of the two regions including ROI and Non-ROI, and get the whole texture feature. The algorithm is tested that the average detection rate of the proposed method is up to the same method using GLCM.

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References

  1. Ramey, N.A., Ying, H.S., Irsch, K.: A novel haploscopic viewing apparatus with a three-axis eye tracker. Journal of AAPOS.2008.01.019

    Google Scholar 

  2. Clarke, A.H., Ditterich, J., Druen, K., Schonfeld, U., Steineke, C.: Using high frame rate CMOS sensors for three-dimensional eye tracking. Behav. Res. Methods Instrum. Comput. 34, 549–560 (2002)

    Article  Google Scholar 

  3. Hough, P.V.C.: Methods and means for recognizing complex patterns. U.S. Patent 3069654 (1962)

    Google Scholar 

  4. Duda, R.O., Hart, P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)

    Article  MATH  Google Scholar 

  5. Tsuji, S., Matsumoto, F.: Detection of ellipses by a modified Hough transform. IEEE Trans. Comput. 27, 777–781 (1978)

    Article  Google Scholar 

  6. Moore, S.T., Haslwanter, T., Curthoys, I.S., Smith, S.T.: A geometric basis for measurement of three-dimensional eye position using image processing. Vision Res. 36, 445–459 (1996)

    Article  Google Scholar 

  7. Cheng, S.-C., Chen, M.-Y., Chang, H.-Y., Chou, T.-C.: Semantic-based facial expression recognition using analytical hierarchy process (2007)

    Google Scholar 

  8. Haralick, R.M., Shanmugam, K., Dinstein, I.: IEEE Trans. Syst. Man Cybern.  3, 610–621 (1973)

    Google Scholar 

  9. Tomita, F., Tsuji, S.: Computer Analysis of Visual Textures. Kluwer Academic Publishing, Massachusetts (1990)

    Book  MATH  Google Scholar 

  10. Weszka, J.S., Dyer, C.R., Rosenfeld, A.: IEEE Trans. Syst. Man Cybern.  5, 269–285 (1976)

    Google Scholar 

  11. Al-Janobi, A.: Pattern Recogn.  34, 171–180 (2001)

    Google Scholar 

  12. Schmid, C., Mohr, R.: IEEE Transactions on Pattern Analysis and Machine Intelligence.  19(5), 530 (1997)

    Google Scholar 

  13. Tian, Q., Sebe, N., Lew, M.S., et al.: Journal of Electronic Imaging.  10(4), 835 (2001)

    Google Scholar 

  14. Moghaddam, B., Biermann, H., Margaritis, D.: Defining image content with multiple regions of interest. In: Proc. IEEE Workshop on Content-Based Access of Image and Video Libraries (CVPR 1999), p. 350 (1999)

    Google Scholar 

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Li, H., Men, L., Chen, J. (2008). A Method of the Extraction of Texture Feature. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_41

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  • DOI: https://doi.org/10.1007/978-3-540-92137-0_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92136-3

  • Online ISBN: 978-3-540-92137-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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