KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Visual Saliency Detection Based on color Frequency Features under Bayesian framework

  • Ayoub, Naeem (Department of Computer science and technology, Dalian University of technology) ;
  • Gao, Zhenguo (Department of Computer science and technology, Dalian University of technology) ;
  • Chen, Danjie (College of software, Beijing institute of technology) ;
  • Tobji, Rachida (Department of Computer science and technology, Dalian University of technology) ;
  • Yao, Nianmin (Department of Computer science and technology, Dalian University of technology)
  • Received : 2017.07.05
  • Accepted : 2017.09.25
  • Published : 2018.02.28
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Abstract

Saliency detection in neurobiology is a vehement research during the last few years, several cognitive and interactive systems are designed to simulate saliency model (an attentional mechanism, which focuses on the worthiest part in the image). In this paper, a bottom up saliency detection model is proposed by taking into account the color and luminance frequency features of RGB, CIE $L^*a^*b^*$ color space of the image. We employ low-level features of image and apply band pass filter to estimate and highlight salient region. We compute the likelihood probability by applying Bayesian framework at pixels. Experiments on two publically available datasets (MSRA and SED2) show that our saliency model performs better as compared to the ten state of the art algorithms by achieving higher precision, better recall and F-Measure.

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References

  1. C. Koch and S. Ullman, "Shifts in selective visual attention: towards the underlying neural circuitry," Human Neurobiology, vol. 4, no.4, pp. 219-227, 1985.
  2. L. Itti, C. Koch and E., Niebur, "A model of saliency based visual attention for rapid scene analysis," IEEE Transactions on Pattern analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254-1259, November, 1998. https://doi.org/10.1109/34.730558
  3. A. M. Treisman and G. Gelade, "A feature-integration theory of attention," Cognitive Psychology, vol. 12, no. 1, pp. 97-136, January, 1980. https://doi.org/10.1016/0010-0285(80)90005-5
  4. M. Jian, K. M. Lam and J. Dong, "Facial-feature detection and localization based on a hierarchical scheme," Information Sciences, vol. 262, pp. 1 - 14, March, 2014. https://doi.org/10.1016/j.ins.2013.12.001
  5. M. W. Jian, J. Y. Dong and J. Ma, "Image retrieval using wavelet-based salient regions," The Imaging Science Journal, vol. 59, no. 4, pp. 219-231, 2011. https://doi.org/10.1179/136821910X12867873897355
  6. P. Khuwuthyakorn, A Robles-Kelly and J. Zhou, "Object of interest detection by saliency learning," in Proc. of Proceedings of the European Conference on Computer Vision, Heraklion, Crete, Greece, pp. 636-649, September, 2010.
  7. L. Shi, J. Wang, L. Xu, H. Lu and C. Xu, "Context saliency based image summarization," in Proc. of Proceedings of the IEEE International Conference on Multimedia and Expo, New York, USA, pp. 270-273, July, 2009.
  8. M. Donoser, M. Urschler, M. Hirzer and H. Bischof, "Saliency driven total variation segmentation," in Proc. of Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan, pp. 817-824, September, 2009.
  9. R. Achanta, F. Estrada, P. Wils and S. Susstrunk, "Salient region detection and segmentation," in Proc. of Proceedings of the International Conference on Computer Vision Systems, Santorini, Greece, pp. 66-75, May, 2008.
  10. C. Guo, L. Zhang, "A Novel Multiresolution Spatiotemporal Saliency Detection Model and Applications in Image and Video Compression," IEEE Transactions on Image Processing, vol. 19, no. 1, pp. 185-198, January, 2010. https://doi.org/10.1109/TIP.2009.2030969
  11. Y. Nagai, "From bottom-up visual attention to robot action learning," in Proc. of Proceedings of the IEEE International Conference on Development and Learning, Shanghai, China, pp. 1-6, June, 2009.
  12. J. Zhang, , M. Wang, S. Zhang, X. Li and X. Wu, "Spatiochromatic Context Modeling for Color Saliency Analysis," IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 6, pp. 1177-1189, June, 2016. https://doi.org/10.1109/TNNLS.2015.2464316
  13. T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang and X. Y. Shum, "Learning to detect a salient object," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 2, pp. 353-367, February, 2011. https://doi.org/10.1109/TPAMI.2010.70
  14. M. Cheng, N.J. Mitra, X. Huang, P. H. S. Torr and S. H. Hu, "Global contrast based salient region detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 3, pp. 569 - 582, March, 2015. https://doi.org/10.1109/TPAMI.2014.2345401
  15. R. Achanta, S. Hemami, F. Estrada and S. Susstrunk, "Frequency tuned salient region detection," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, pp. 1597- 1604, June, 2009.
  16. J. Harel, C. Koch and P. Perona, "Graph-based visual saliency," Advances in Neural Information Processing Systems, pp. 545- 552, December, 2007.
  17. M. Jian, K. M. Lam, J. Dong and L. Shen, "Visual-Patch-Attention-Aware Saliency Detection." IEEE Transactions on Cybernetics, vol. 45, no. 8, pp. 1575 - 1586, August, 2015. https://doi.org/10.1109/TCYB.2014.2356200
  18. M. Jian, Q. Qi, J. Dong, S. Sun and K.M. Lam, "Saliency detection using quaternionic distance based weber descriptor and object cues," in Proc. of Signal and Information Processing Association Annual Summit and Conference (APSIPA), Asia-Pacific, jeju, south Korea, December 13-16, 2016.
  19. V. Setlur, T. Lechner, M. Nienhaus and B. Gooch, "Retargeting Images and Video for Preserving Information Saliency," IEEE Computer Graphics and Applications, vol. 27, no. 5, pp. 80 - 88, September, 2007. https://doi.org/10.1109/MCG.2007.133
  20. M. Guttmann, L. Wolf and C. O. Danny, "Content aware video manipulation," Computer Vision and Image Understanding, vol. 115, no. 12, pp. 1662-1678, December, 2011. https://doi.org/10.1016/j.cviu.2011.05.010
  21. B. C. Ko and J. Y. Nam, "Object-of-interest image segmentation based on human attention and semantic region clustering," Journal of the Optical Society of America A, vol. 23, no. 10, pp. 2462-2470, 2006. https://doi.org/10.1364/JOSAA.23.002462
  22. J. Han, K.N. Ngan, M. Li and H. J. Zhang, "Unsupervised extraction of visual attention objects in color images," IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 1, pp. 141-145, January, 2006. https://doi.org/10.1109/TCSVT.2005.859028
  23. U. Rutishauser, D. Walther, C. Koch and P. Perona, "Is bottom-up attention useful for object recognition?" in Proc. of Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04), Washington, DC, USA, July, 2004.
  24. J. S. Kim, J. H. Kim and C. S. Kim, "Adaptive image and video retargeting technique based on fourier analysis," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'09), Miami, FL, USA, pp. 1730-1737, June, 2009.
  25. D. J. Field, "Relations between the statistics of natural images and the response properties of cortical cells," Journal of the Optical Society of America. A, vol. 4, no.12, pp. 2379-2394, December, 1987. https://doi.org/10.1364/JOSAA.4.002379
  26. J. V. D. Weijer, T. Gevers and A. D. Bagdanov, "Boosting color saliency in image feature detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 150-156. January, 2006. https://doi.org/10.1109/TPAMI.2006.3
  27. C. Harris and M. Stephens, "A combined corner and edge detector," in Proc. of Fourth Alvey Vision Conference, pp. 147-151, 1988.
  28. P. Kovesi, "Image features from phase congruency," Videre: Journal of Computer Vision Research, vol. 1, pp. 1-30, June, 1995.
  29. E. Rahtu, J. Kannala, M. Salo and J. Heikkila, "Segmenting salient objects from images and videos," in Proc. of Proceedings of European Conference on Computer Vision (ECCV'10), Crete, Greece, pp. 366-379, September, 2010.
  30. Y. Xie, H. Lu and M. H. Yang, "Bayesian saliency via low and mid level cues," IEEE Transactions on Image Processing, vol. 22, no. 5, pp. 1689 -1698, May, 2013. https://doi.org/10.1109/TIP.2012.2216276
  31. L. Zhang, M. H. Tong, T. K. Marks, H. Shan and G. W. Cottrell, "SUN: A Bayesian framework for saliency using natural statistics," Journal of Vision, vol. 8, no. 7, pp. 1-20, December, 2008.
  32. T. N. Vikram, M. Tscherepanow and B. Wrede, "A saliency map based on sampling an image into random rectangular regions of interest," Pattern Recognition, vol. 45, no. 9, pp. 3114-3124, September, 2012. https://doi.org/10.1016/j.patcog.2012.02.009
  33. R. Achanta and S. Susstrunk, "Saliency detection using maximum symmetric surround," in Proc. of Proceedings of IEEE International Conference on Image Processing, Hong Kong, pp. 2653-2656, September, 2010.
  34. H. J. Seo, P. Milanfar, "Static and space-time visual saliency detection by self-resemblance," Journal of Vision, vol. 9, no.12, pp.1-27, November, 2009.
  35. T. N. Vikram, M. Tscherepanow and B. Wrede, "A random center surround bottom up visual attention model useful for salient region detection," in Proc. of Proceedings of the IEEE Workshop on Applications of Computer Vision, Kona, HL, USA, pp. 166-173, February, 2011.
  36. W. H. Tsai, "Moment-preserving thresholding: a new approach, Computer Vision," Graphics and Image Processing, vol. 29, no. 3, pp. 377-393, March, 1985. https://doi.org/10.1016/0734-189X(85)90133-1
  37. R. B. S. Alpert, M. Galun, and A. Brandt, "Image segmentation by probabilistic bottom-up aggregation and cue integration," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, July, 2007.

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