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

The Institute of Electronics and Information Engineers (대한전자공학회)
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An Artificial Visual Attention Model based on Opponent Process Theory for Salient Region Segmentation

돌출영역 분할을 위한 대립과정이론 기반의 인공시각집중모델

  • Jeong, Kiseon (Department of Electronic Engineering, Chonbuk National University) ;
  • Hong, Changpyo (Department of Electronic Engineering, Chonbuk National University) ;
  • Park, Dong Sun (Department of Electronic Engineering, Chonbuk National University)
  • Received : 2014.02.03
  • Accepted : 2014.07.09
  • Published : 2014.07.25
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Abstract

We propose an novel artificial visual attention model that is capable of automatic detection and segmentation of saliency region on natural images in this paper. The proposed model is based on human visual perceptions in biological vision and contains there are main contributions. Firstly, we propose a novel framework of artificial visual attention model based on the opponent process theory using intensity and color features, and an entropy filter is designed to perceive salient regions considering the amount of information from intensity and color feature channels. The entropy filter is able to detect and segment salient regions in high segmentation accuracy and precision. Lastly, we also propose an adaptive combination method to generate a final saliency map. This method estimates scores about intensity and color conspicuous maps from each perception model and combines the conspicuous maps with weight derived from scores. In evaluation of saliency map by ROC analysis, the AUC of proposed model as 0.9256 approximately improved 15% whereas the AUC of previous state-of-the-art models as 0.7824. And in evaluation of salient region segmentation, the F-beta of proposed model as 0.7325 approximately improved 22% whereas the F-beta of previous state-of-the-art models.

본 논문에서는 자연영상에 대한 돌출영역을 자동으로 검출하고 이를 분할하기 위한 새로운 인공시각집중모델을 제안한다. 제안된 모델은 인간의 생물학적 시각인지 기반이며 주된 특징은 다음과 같다. 먼저 영상의 강도특징과 색상특징을 사용하는 대립과정이론 기반의 새로운 인공시각집중모델의 구조를 제안하고, 돌출영역을 인지하기 위해 영상의 강도 및 색상 특징채널의 정보량을 고려하는 엔트로피 필터를 설계하였다. 엔트로피 필터는 높은 정확도와 정밀도로 돌출영역에 대해 검출 및 분할이 가능하다. 마지막으로 최종 돌출지도를 효율적으로 구성하기 위한 적응 조합 방법 또한 제안되었다. 이 방법은 각 인지 모델로부터 검출된 강도 및 색상 가시성지도에 대하여 평가하며 평가된 점수로부터 얻어진 가중치를 이용해 가시성 지도들을 조합한다. 돌출지도에 대해 ROC분석을 이용한 AUC를 측정한 결과 기존 최신의 모델들은 평균 0.7824의 성능을 나타낸 반면 제안된 모델의 AUC는 0.9256으로서 약 15%의 성능 개선을 보였다. 또한 돌출영역 분할에 대해 F-beta를 측정한 결과 기존 최신의 모델은 0.5178이고 제안된 모델은 0.7325로서 분할 성능 또한 약 22%의 성능 개선을 보였다.

Keywords

References

  1. K. Koch, J. McLean, R. Segev, Michael A. Freed, Micheal J. Berry, V. Balasubramanian, and P. Sterling, "How Much the Eye Tells the Brain," Cutt. Biol., Vol. 16, no. 14, pp. 1428-1434, 2006.
  2. L. Itti, "Models of Bottom-Up and Top-Down Visual Attention," PhD Thesis, California Inst. Of Technology, 2000.
  3. http://en.wikipedia.org/wiki/Visual_system
  4. A. Alexandre, O. Raphael, V. Pierre, "FREAK: Fast Retina Keypoint," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 510-517, Rhode Island, Providence, USA, 2012.
  5. A. Borji, L. Itti, "State-of-the-art in Visual Attention Modeling," IEEE Trans. Pattern Analysis and Mchine Intelligence, Vol 35, no. 1, pp. 185-207, 2013. https://doi.org/10.1109/TPAMI.2012.89
  6. K. Duncan, S. Sarkar, "Saliency in images and video: a brief survey," IEEE Trans. Computer Vision, IET, Vol. 6, Issue 6, pp. 514-523, 2012. https://doi.org/10.1049/iet-cvi.2012.0032
  7. Ajay K. Mishra and Y. Aloimonos, "Active Segmentation," Int'l J. of Humanoid Robotics, Vol. 6, pp. 361-386, 2009. https://doi.org/10.1142/S0219843609001784
  8. S. Frintrop, VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search, Springer, 2006.
  9. P. Viola and Michael. J. Jones, "Robust Real-Time Face Detection," Int'l J. Computer Vision, Vol. 57, Issue 2, pp. 137-154, 2004. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
  10. N. J. Butko and Javier R. Movellan, "Optimal Scanning for Faster Object Detection," in Proc. of Conf. on Computer Vision and Pattern Recognition, Miami, FL, USA pp. 2751-2758, 2009.
  11. Albert A. Salahh, E. Alpaydin, and L. Akrun, "A Selective Attention-Based Method for Visual Pattern Recognition with Application to Handwritten Digit Recognition and Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 24, no. 3, pp. 420-425, 2002. https://doi.org/10.1109/34.990146
  12. D. Walther and C. Koch, "Modeling Attention to Salient Proto-Objects," Neural Networks, Vol. 19, no. 9, pp. 1395-1407, 2006. https://doi.org/10.1016/j.neunet.2006.10.001
  13. S. Frintrop, "General Object Tracking with a Component-Based Target Descriptor," in Proc. of IEEE Int'l Conf. Robotics and Automation, Anchorage, AK, pp. 4541-4536, 2010.
  14. S. Mitri, S. Frintrop, K. Pervolz, H. Surmann and A. Nuchter, "Robust Object Detection at Regions of Interest with an Application in Ball Recognition," in Proc. of IEEE Int'l Conf. of Robotics and Automation, pp. 125-130, 2005.
  15. V. Mahadevan and N. Vasconcelos, "Saliency-Based Discriminant Tracking," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1007-1013, Miami, FL, USA, 2009.
  16. C. Siagian and L. Itti, "Biologically Inspired Robot Vision Localization," IEEE Trans. Robotics, Vol. 25, Issue 4, pp. 861-873, 2009. https://doi.org/10.1109/TRO.2009.2022424
  17. N. Oeurhani, and H. Hagli, "Visual Attention-Based Robot Self-Localization," in Proc. of IEEE Int'l Symp. Computational Intelligence in Robotics and Automation, pp. 308-314, 2005.
  18. S. Baluja and Dean, A. Pemerleau, "Expectation-Based Selective Attention for Visual Attention Monitoring and Control of a Robot Vehicle," Robotics and Autonomous Systems, Vol. 22, pp. 329-344, 1997. https://doi.org/10.1016/S0921-8890(97)00046-8
  19. C. Scheier and S. Egner, "Visual Attention in a Mobile Robot," in Proc. Int'l Symp. on Industrial Electronics, pp. 48-53, Guimaraes, 1997.
  20. L. Itti, C. Koch, and E. Nieber, "A Model of Saliency-based Visual Attention for Rapid Scene Analysis, " IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 20, Issue 11, pp. 1254-1259, 1985.
  21. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, "Frequency-tuned Salient Region Detection," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1597-1604, Miami, FL, USA, 2009.
  22. X. Hou and L. Zhang, "Saliency Detection: A Spectral Residual Approach," in Proc. of Conf. on Computer Vision and Pattern Recognition," pp. 1-8, 2007.
  23. N. Murray, M. Vanrell, X. Otazu, and C. Alejandro Parraga, "Saliency Estimation Using a Non-Parametric Low-Level Vision Model," in Proc. on Conf. IEEE Computer Vision and Pattern Recognition, pp. 433-440, Providence, RI, 2011.
  24. L. Zhang, T. K. Marks, M. H. Tong, H. Shan, G. W. Cottrell, "SUN: A Bayesian Framework for Saliency Using Natural Statistics," Journal of Vision, Vol. 8, no. 7, 2008.
  25. S. Goferman, L. Zelnik-Manor and A. Tal, "Context-Aware Saliency Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 34, Issue 10, pp. 1915-1926, 2012. https://doi.org/10.1109/TPAMI.2011.272
  26. http://en.wikipedia.org/wiki/Opponent_process
  27. Benjamin T. Vincent, R. Braddely, A. Correani, T. Troscianko and U. Leonards, "Do We Look at Lights? Using Mixture Modeling to Distinguish low- and high-level Factors in Natural Image Viewing," Visual Cognition, Vol. 17, Issue 6-7, pp. 856-879, 2009. https://doi.org/10.1080/13506280902916691
  28. B. W. Tatler, "The Central Fixation Bias in Scene Viewing, Selecting an Optimal Viewing Position Independently of Moto Biases and Image Feature Distribution," Journal of Vision, Vol. 7, Issue 14, pp. 1-17, 2008.
  29. M. Bindemann, "Scene and Screen Center Bias Early Eye Movements in Scene Vewing," Vision Redearch, Vol. 50, no. 23 pp. 2577-2587, 2010.