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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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
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)
Hough, P.V.C.: Methods and means for recognizing complex patterns. U.S. Patent 3069654 (1962)
Duda, R.O., Hart, P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)
Tsuji, S., Matsumoto, F.: Detection of ellipses by a modified Hough transform. IEEE Trans. Comput. 27, 777–781 (1978)
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)
Cheng, S.-C., Chen, M.-Y., Chang, H.-Y., Chou, T.-C.: Semantic-based facial expression recognition using analytical hierarchy process (2007)
Haralick, R.M., Shanmugam, K., Dinstein, I.: IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)
Tomita, F., Tsuji, S.: Computer Analysis of Visual Textures. Kluwer Academic Publishing, Massachusetts (1990)
Weszka, J.S., Dyer, C.R., Rosenfeld, A.: IEEE Trans. Syst. Man Cybern. 5, 269–285 (1976)
Al-Janobi, A.: Pattern Recogn. 34, 171–180 (2001)
Schmid, C., Mohr, R.: IEEE Transactions on Pattern Analysis and Machine Intelligence. 19(5), 530 (1997)
Tian, Q., Sebe, N., Lew, M.S., et al.: Journal of Electronic Imaging. 10(4), 835 (2001)
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)
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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
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