전자공학회논문지 (Journal of the Institute of Electronics and Information Engineers)

  • 제54권6호
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  • Pages.72-79
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  • 2017
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  • 2287-5026(pISSN)
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  • 2288-159X(eISSN)
대한전자공학회 (The Institute of Electronics and Information Engineers)
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웨이블릿 부밴드의 상호 정보량을 이용한 세일리언시 검출

Saliency Detection using Mutual Information of Wavelet Subbands

  • Moon, Sang Whan (Dept. Electronics Eng., Pusan National University) ;
  • Lee, Ho Sang (Dept. Electronics Eng., Pusan National University) ;
  • Moon, Yong Ho (Dept. Electronics Eng., Pusan National University) ;
  • Eom, Il Kyu (Dept. Electronics Eng., Pusan National University)
  • 투고 : 2017.01.16
  • 심사 : 2017.05.25
  • 발행 : 2017.06.25
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초록

본 논문에서는 웨이블릿 부밴드의 상호 정보량을 이용한 새로운 세일리언시 검출 방법을 제시한다. 본 논문의 방법은 웨이블릿 고주파 계수에 대한 승수와 가우시안 블러링을 이용하여 중간 세일리언시 지도를 형성한다. 웨이블릿 방향에 따른 세 개의 중간 세일리언시 지도를 방향별로 결합한 후 최소 엔트로피를 가지는 주 방향성 성분을 찾는다. 최소 엔트로피를 가지는 부밴드를 중심으로 각 부밴드의 상호 정보량을 구하고, 이를 이용한 가중치를 계산하고, Minkowski 합을 이용하여 최종 세일리언시를 검출한다. CAT2000 및 ECSSD 데이터베이스 대한 실험 결과, 본 논문의 방법은 기존 방법과 비교하여 적은 계산시간으로 ROC 및 AUC 관점에서 우수한 검출 결과를 보였다.

In this paper, we present a new saliency detection algorithm using the mutual information of wavelet subbands. Our method constructs an intermediate saliency map using the power operation and Gaussian blurring for high-frequency wavelet coefficients. After combining three intermediate saliency maps according to the direction of wavelet subband, we find the main directional components using entropy measure. The amount of mutual information of each subband is obtained centering on the subband having the minimum entropy The final saliency map is detected using Minkowski sum based on weights calculated by the mutual information. As a result of the experiment on CAT2000 and ECSSD databases, our method showed good detection results in terms of ROC and AUC with few computation times compared with the conventional methods.

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참고문헌

  1. Fang, Yuming, et al. "A visual attention model combining top-down and bottom-up mechanisms for salient object detection." 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011.
  2. Treisman, Anne M., and Garry Gelade. "A feature-integration theory of attention." Cognitive psychology vol. 12, no. 1, pp. 97-136, 1980. https://doi.org/10.1016/0010-0285(80)90005-5
  3. M. Land, N. Mennie, and J. Rusted, "The Roles of Vision and Eye Movements in the Control of Activities of Daily Living," Perception, vol. 28, no. 11, pp. 1311-1328, 1999. https://doi.org/10.1068/p2935
  4. Furuya, Takahiko, and Ryutarou Ohbuchi. "Visual saliency weighting and cross-domain manifold ranking for sketch-based image retrieval." International Conference on Multimedia Modeling. Springer International Publishing, 2014.
  5. Zhu, Xianqiang, et al. "A new approach for interesting local saliency features definition and its application to remote sensing imagery retrieval." Geomatics and Information Science of Wuhan University vol. 38, no. 6, pp. 652-655, 2013.
  6. Chang, Kai-Yueh, Tyng-Luh Liu, and Shang-Hong Lai. "From co-saliency to co-segmentation : An efficient and fully unsupervised energy minimization model." Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, pp. 2129-2136, 2011.
  7. Qin, Chanchan, et al. "Integration of the saliency-based seed extraction and random walks for image segmentation." Neurocomputing vol. 129 pp. 378-391, 2014. https://doi.org/10.1016/j.neucom.2013.09.021
  8. Ding, Zhenghu, et al. "An approach for visual attention based on biquaternion and its application for ship detection in multispectral imagery." Neurocomputing vol. 76, no. 1, pp. 9-17, 2012. https://doi.org/10.1016/j.neucom.2011.05.027
  9. Fang, Yuming, et al. "Saliency detection in the compressed domain for adaptive image retargeting." IEEE Transactions on Image Processing vol. 21, no. 9, pp. 3888-3901, 2012. https://doi.org/10.1109/TIP.2012.2199126
  10. Bhatnagar, Gaurav, and QM Jonathan Wu. "An image fusion framework based on human visual system in framelet domain." International Journal of Wavelets, Multiresolution and Information Processing vol. 10, no. 01, pp. 1250002, 2012. https://doi.org/10.1142/S0219691311004444
  11. Kadir, Timor, and Michael Brady. "Saliency, scale and image description." International Journal of Computer Vision vol. 45, no. 2, pp. 83-105, 2001. https://doi.org/10.1023/A:1012460413855
  12. Itti, Laurent, Christof Koch, and Ernst 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, 1998. https://doi.org/10.1109/34.730558
  13. 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
  14. 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.
  15. 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.
  16. 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
  17. 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.
  18. 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.
  19. W. Hou, X. Gao, D. Tao, and X. Li, "Visual saliency detection using information divergence," Pattern Recognit., vol 46, pp. 2658-2669, 2015.
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. Lin, Yuewei, et al. "A visual-attention model using earth mover's distance-based saliency measurement and nonlinear feature combination." IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 2, pp. 314-328, 2013. https://doi.org/10.1109/TPAMI.2012.119
  26. M. To, P.G. Lovell, T. Troscianko, and D.J. Tolhurst, "Summation of Perceptual Cues in Natural Visual Scenes," Proc. Royal Soc. B, vol. 275, no. 1649, pp. 2299-2308, 2008. https://doi.org/10.1098/rspb.2008.0692
  27. Borji, Ali, and Laurent Itti. "Cat2000: A large scale fixation dataset for boosting saliency research." arXiv preprint arXiv:1505.03581, 2015.
  28. Shi, Jianping, et al. "Hierarchical image saliency detection on extended cssd." IEEE transactions on pattern analysis and machine intelligence vol. 38, no. 4, pp. 717-729, 2016. https://doi.org/10.1109/TPAMI.2015.2465960
  29. 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