DOI QR코드

DOI QR Code

Saliency Detection Using Entropy Weight and Weber's Law

엔트로피 가중치와 웨버 법칙을 이용한 세일리언시 검출

  • Lee, Ho Sang (Dept. Electronics Eng., Pusan National University) ;
  • Moon, Sang Whan (Dept. Electronics Eng., Pusan National University) ;
  • Eom, Il Kyu (Dept. Electronics Eng., Pusan National University)
  • Received : 2016.05.04
  • Accepted : 2016.12.30
  • Published : 2017.01.25

Abstract

In this paper, we present a saliency detection method using entropy weight and Weber contrast in the wavelet transform domain. Our method is based on the commonly exploited conventional algorithms that are composed of the local bottom-up approach and global top-down approach. First, we perform the multi-level wavelet transform for the CIE Lab color images, and obtain global saliency by adding the local Weber contrasts to the corresponding low-frequency wavelet coefficients. Next, the local saliency is obtained by applying Gaussian filter that is weighted by entropy of wavelet high-frequency subband. The final saliency map is detected by non-lineally combining the local and global saliencies. To evaluate the proposed saliency detection method, we perform computer simulations for two image databases. Simulations results show the proposed method represents superior performance to the conventional algorithms.

본 논문에서는 웨이블릿 변환 영역에서 엔트로피 가중치와 웨버 대비 도를 이용한 세일리언시 검출 방법을 제안한다. 본 논문의 방법은 기존의 일반적인 방법과 마찬가지로 국부적인 세일리언시를 결정하는 상향식 검출과 전역적인 세일리언시를 구성하는 하향식 검출을 결합하는 구조를 가진다. 먼저, CIE Lab 컬러 영상에 대하여 웨이블릿 변환을 수행하고, 저주파 부밴드에 대하여 웨버 대비도 계산하고 이를 저주파 계수에 부가하여 전역 세일리언시를 구한다. 다음으로, 고주파 부밴드의 엔트로피를 이용한 가중치를 가우시안 필터에 적용하여 국부 세일리언시를 구한다. 마지막으로 국부 세일리언시와 전역 세일리언시의 비선형 결합을 통하여 최종 세일리언시를 검출한다. 제안 방법의 성능 평가를 위해 2개의 영상 데이터베이스에 대하여 모의실험을 수행하였다. 기존의 방법과 비교하여 본 논문의 방법은 우수한 세일리언시 검출 결과를 나타내었다.

Keywords

References

  1. V. Mahadevan, and N. Vasconcelos, "Biologically inspired object tracking using center-surround saliency mechanisms," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 3. pp. 541-554, 2013. https://doi.org/10.1109/TPAMI.2012.98
  2. Y. Fang, Z. Chen, W. Lin, and C. W. Lin, "Saliency detection in the compressed domain for adaptive image retargeting," IEEE Trans. Image Process., vol. 21, no. 9, pp. 3888-3901. 2012. https://doi.org/10.1109/TIP.2012.2199126
  3. H. Yang, Y. Li, W. Li, X. Wang, and F. Yang, "Content-based image retrieval using local visual attention feature," J. Vis. Commun. Image Represent., vol. 25, no. 6, pp. 1308-1323, 2014. https://doi.org/10.1016/j.jvcir.2014.05.003
  4. C. Huang, F. Meng, W. Luo, and S. Zhu, "Bird breed classification and annotation using saliency based graphical model," J. Vis. Commun. Image Represent., vol. 25, no. 6, pp. 1299-1307, 2014. https://doi.org/10.1016/j.jvcir.2014.05.002
  5. C. Jung, C. and Kim, "A unified spectral-domain approach for saliency detection and its application to automatic object segmentation," IEEE Trans. Image Process., vol. 21, no.3, pp. 1272-1283, 2012. https://doi.org/10.1109/TIP.2011.2164420
  6. A. Toet, "Computational versus psychophysical bottom-up image saliency: a comparative evaluation study," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 11, pp. 2131-2146, 2011. https://doi.org/10.1109/TPAMI.2011.53
  7. L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 11. pp. 1264-259, 1998.
  8. O. L. Meur, P. L Callet, D. Barba, and D. Thoreau, "A coherent computational approach to model bottom-up visual attention," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 5. pp. 802-8179, 2006. https://doi.org/10.1109/TPAMI.2006.86
  9. S. Goferman, L. Zelnik-Manor, and A. Tal, "Context-aware saliency detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 10. pp. 1915-1926, 2012. https://doi.org/10.1109/TPAMI.2011.272
  10. X. Hou, and L. Zhang, "Saliency detection: a spectral residual approach," Proceeding of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2007.
  11. C. Guo, Q. Ma, and L. Zhang, "Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform," Proceeding of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
  12. N. Imamoglu, W. Lin, and Y. Fang, "A saliency detection model using low-level features based on wavelet transform," IEEE Trans. Multimedia, vol. 15, no. 1, pp. 96-105, 2013. https://doi.org/10.1109/TMM.2012.2225034
  13. N. Murray, M. Vanrell, X. Otazu, and C. A. Parraga, "Saliency estimation using a non-parametric low-level vision model," Proceeding of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 433-440, 2011.
  14. W. Wang, Y. Wang, Q. Huang, and W. Gao, "Measuring visual saliency by site entropy rate," Proceeding of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 2368-2375, 2010.
  15. W. Hou, X. Gao, D. Tao, and X. Li, "Visual aliency detection using information divergence," Pattern Recognit., vol 46, pp. 2658-2669, 2015.
  16. X. Ma, X. Xie, K. M. Lam, and Y. Zhong, "Efficient saliency analysis based on wavelet transform and entropy theory," J. Vis. Commun. Image Represent., vol. 30, pp. 201-207, 2015. https://doi.org/10.1016/j.jvcir.2015.04.008
  17. X. Ma, X. Xie, K. M. Lan, J. Hu, and Y. Zhong, "Saliency detection based on singular value decomposition," J. Vis. Commun. Image Represent., vol. 32, pp. 95-106, 2015. https://doi.org/10.1016/j.jvcir.2015.08.003
  18. Y. Fang, W. Lin, Z. Chen, C. W. Lin, and C. Deng, "Visual acuity inspired saliency detection by using sparse features," Inf. Sci., vol. 309, pp. 1-10, 2015. https://doi.org/10.1016/j.ins.2015.03.004
  19. Y. Wo, X. Chen, and G. Han, "A saliency detection model using aggregation degree of color and texture," Signal process. Image commun., vol. 30. pp. 121-136, 2015. https://doi.org/10.1016/j.image.2014.10.004
  20. J. Lou, M. Ren, and H. Wang, "Regional principal color based saliency detection," plos one, vol. 9, no. 11, e112475, 2014. https://doi.org/10.1371/journal.pone.0112475
  21. M. M. Cheng, N. Mitra, X. Huang, P. Torr, and S. M. Hu, "Global contrast based salient region detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 3, pp. 569-582, 2015. https://doi.org/10.1109/TPAMI.2014.2345401
  22. Z. Chen, H. Wang, L. Zhang, Y. Yan, and H. M. Lao, "Visual saliency detection based on homology similarity and an experimental evaluation." J. Vis. Commun. Image Represent., vol. 40, pp. 251-264, 2016. https://doi.org/10.1016/j.jvcir.2016.06.013
  23. A. Borji, and L. Itti, "CAT2000: A large scale fixation dataset for boosting saliency research," arXiv preprint arXiv:1505.03581, 2015.
  24. http://mftp.mmcheng.net/
  25. C. D. Brown, and H. T. Davis, "Receiver operating characteristics curves and related decision measures: A tutorial," Chemometer, Intell. Lab., vol. 80, no. 1, pp. 24-38, 2006. https://doi.org/10.1016/j.chemolab.2005.05.004
  26. A. Cohen, I. Daubhecies, and J. C. Feauveau, "Biorthogonal bases of compactly supported wavelets," Comm. Pure Appl. Math., vol. 45, no. 5, pp. 485-560, 1992. https://doi.org/10.1002/cpa.3160450502