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http://dx.doi.org/10.9717/kmms.2015.18.9.1008

Extraction of an Effective Saliency Map for Stereoscopic Images using Texture Information and Color Contrast  

Kim, Seong-Hyun (Dept of Digitalmedia, Catholic University of Korea)
Kang, Hang-Bong (Dept of Digitalmedia, Catholic University of Korea)
Publication Information
Journal of Korea Multimedia Society / v.18, no.9, 2015 , pp. 1008-1018 More about this Journal
Abstract
In this paper, we propose a method that constructs a saliency map in which important regions are accurately specified and the colors of the regions are less influenced by the similar surrounding colors. Our method utilizes LBP(Local Binary Pattern) histogram information to compare and analyze texture information of surrounding regions in order to reduce the effect of color information. We extract the saliency of stereoscopic images by integrating a 2D saliency map with depth information of stereoscopic images. We then measure the distance between two different sizes of the LBP histograms that are generated from pixels. The distance we measure is texture difference between the surrounding regions. We then assign a saliency value according to the distance in LBP histogram. To evaluate our experimental results, we measure the F-measure compared to ground-truth by thresholding a saliency map at 0.8. The average F-Measure is 0.65 and our experimental results show improved performance in comparison with existing other saliency map extraction methods.
Keywords
Stereo Image; Saliency Map; Local Binary Pattern;
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1 Y. Niu, Y. Geng, X. Li, and F. Liu, "Leveraging Stereopsis for Saliency Analysis," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 454-461, 2012.
2 K. Yoon and I.S. Kweon, "Adaptive Support-weight Approach for Correspondence Search," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 4, pp. 650-656, 2006.   DOI
3 B.J. Balas, "Texture Synthesis and Perception: Using Computational Models to Study Texture Representations in the Human Visual System," Vision Research, Vol. 46, No. 3, pp. 299-309, 2006.   DOI
4 T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 971-987, 2002.   DOI
5 H. Jung, K. Cho, and K. Han, “The Impact of Brightness, Polarity, and Hue Difference on Legibility and Emotional Effect of Word in Visual Display,” Journal of Korean Society for Cognitive Science, Vol. 17, No. 4, pp. 337-356, 2006.
6 Z. Yang and H. Ai, "Demographic Classification with Local Binary Patterns," Advances in Biometrics, Vol. 4642, pp. 464-473, 2007.   DOI
7 J. Ren, X. Jiang, and J. Yuan, "Noise-Resistant Local binary Pattern with an Embedded Error-Correction Mechanism," IEEE Transactions on Image Processing, Vol. 22, No. 10, pp. 4049-4060, 2013.   DOI
8 Z. Guo, L. Zhang, and D. Zhang, "Rotation Invariant Texture Classification using LBP Variance (LBPV) with Global Matching," Pattern Recognition, Vol. 43, No. 3, pp. 706-719, 2010.   DOI
9 J. Häkkinen, T. Kawai, J. Takatalo, R. Mitsuya, and G. Nyman, "What do people look at when they watch Stereoscopic Movies?," IS&T/SPIE Electronic Imaging, Vol. 7524, pp. 2010.
10 R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, "Frequency-tuned Salient Region Detection," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597-1604, 2009.
11 J. Zhu, Y. Qiu, R. Zhang, J. Huang, and W. Zhang, "Top-Down Saliency Detection via Contextual Pooling," Journal of Signal Processing Systems, Vol. 74, No. 1, pp. 33-46, 2014.   DOI
12 L. Jansen, S. Onat, and P. König, "Influence of Disparity on Fixation and Saccades in Free Viewing of Natural Scenes," Journal of Vision, Vol 9, No. 1, pp. 1-19, 2009.   DOI
13 S. Goferman, L. Zelnik-Manor, and A. Tal, "Context-Aware Saliency Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 10, pp. 1915-1926, 2012.   DOI
14 J. Li, M.D. Levine, X. An, X. Xu, and H. He, "Visual Saliency based on Scale-Space Analysis in the Frequency Domain," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 4, pp. 996-1010, 2013.   DOI
15 J. Harel, C. Koch, and P. Perona, "Graph-Based Visual Saliency," Proceeding of Conferences on Neural Information Processing Systems, pp. 545-552, 2006.
16 P. Anandan, "A Computational Framework and an Algorithm for the Measurement of Visual Motion," International Journal of Computer Vision, Vol. 2, No. 3, pp. 283-310, 1989.   DOI
17 D. Terzopoulos, “Regularization of Inverse Visual Problems Involving Discontinuities,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 4, pp. 413–424, 1986.   DOI
18 S. Birchfield and C. Tomasi, “A Pixel Dissimilarity Measure That is Insensitive to Image Sampling,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 4, pp. 401-406, 1998.   DOI
19 D. Marr and T. Poggio, “Cooperative Computation of Stereo Disparity,” Science, Vol. 194, No. 4262, pp. 283-287, 1976.   DOI
20 S.M. Kim, C.H. Park and J.C. Namkung, "Face Feature Extraction Method Through Stereo Image's Matching Value," Journal of Korea Multimedia Society, Vol. 8, No. 4, pp. 461-472, 2005.
21 J.H. Reynolds and R. Desimone, "Interacting Roles of Attention and Visual Salience in V4," Neuron, Vol. 37, No. 5, pp. 853-863, 2003.   DOI
22 J. Wang, M.P.D. Silva, P.L. Callet, and V. Ricordel, "Computational Model of Stereoscopic 3D Visual Saliency," IEEE Transactions on Image Processing, Vol. 22, Issue 6, pp. 2151-2165, 2013.   DOI
23 M. Cheng, G. Zhang, N.J. Mitra, X. Huang, and S. Hu, "Global Contrast Based Salient Region Detection," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 409-416, 2011.