Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Extraction of Attentive Objects Using Feature Maps

특징 지도를 이용한 중요 객체 추출

  • Park Ki-Tae (Department of Computer Science and Engineering, Hanyang University) ;
  • Kim Jong-Hyeok (KODICOM Co., LTD) ;
  • Moon Young-Shik (Department of Computer Science and Engineering, Hanyang University)
  • Published : 2006.09.01
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Abstract

In this paper, we propose a technique for extracting attentive objects in images using feature maps, regardless of the complexity of images and the position of objects. The proposed method uses feature maps with edge and color information in order to extract attentive objects. We also propose a reference map which is created by integrating feature maps. In order to create a reference map, feature maps which represent visually attentive regions in images are constructed. Three feature maps including edge map, CbCr map and H map are utilized. These maps contain the information about boundary regions by the difference of intensity or colors. Then the combination map which represents the meaningful boundary is created by integrating the reference map and feature maps. Since the combination map simply represents the boundary of objects we extract the candidate object regions including meaningful boundaries from the combination map. In order to extract candidate object regions, we use the convex hull algorithm. By applying a segmentation algorithm to the area of candidate regions to separate object regions and background regions, real object regions are extracted from the candidate object regions. Experiment results show that the proposed method extracts the attentive regions and attentive objects efficiently, with 84.3% Precision rate and 81.3% recall rate.

본 논문에서는 컬러 영상에서 배경의 복잡도와 객체의 위치에 관계없이 영상 내에 존재하는 중요 객체를 자동으로 추출하는 방법을 제안한다. 제안하는 방법은 중요 객체를 추출하기 위해 에지(edge) 정보와 색상(color) 정보를 이용한 특징 지도를 사용한다. 또한, 효과적인 객체 추출을 위해서 참조 지도(reference map)를 제안한다. 참조 지도를 생성하기 위해서는 영상에서 사람의 시각에 두드러지게 구분되는 영역을 표현하는 특징 지도(feature map)를 먼저 생성한다. 그런 다음, 특징 지도들을 효과적으로 결합하여 배경의 영향을 최소화 하면서, 중요 객체가 존재할 확률이 높은 영역들을 포함하는 참조 지도를 생성한다. 특징 지도를 생성하기 위해서는 밝기 차 정보를 나타내는 에지와 YCbCr 컬러와 HSV 컬러 공간에서의 색상 성분을 사용하며, 특징 지도에 대한 생성 방법은 영상 내에서 밝기차이와 색상차이에 의해서 나타나는 경계 부분을 추출하는 방법을 사용한다. 최종적으로 중요 객체가 존재하는 영역을 나타내기 위해서 참조 지도와 특징 지도들을 결합한 결합 지도(combination map)를 생성한다. 결합 지도는 중요 객체의 외곽선 정보만을 표현하기 때문에, 객체 전체를 표현할 수 있는 객체 후보 영역을 추출하는데, 이를 위해서는 객체 후보 영역을 추출하기 위해서 convex hull 알고리즘을 사용한다. Convex hull 알고리즘에 의해서 추출된 영역은 여전히 배경 부분을 포함하고 있으므로, 영상 분할 방법을 적용하여 배경을 제거한 후 영상에서의 중요 객체를 추출한다. 제안한 알고리즘의 성능을 실험적으로 확인한 결과, 평균적으로 84.3%의 정확율과 81.3%의 재현율의 성능을 보였다.

Keywords

References

  1. Y. Rui and T.S.Huang, 'Image Retrieval : Current Techniques, Promising Directions, and Open Issues,' Jouranl of Visual Communication and Image Representation, vol. 10, pp.39-62, 1999 https://doi.org/10.1006/jvci.1999.0413
  2. A.W.M Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, 'Content Based Image Retrieval at The End of The Early Years,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol.22, pp.1349-1380, Dec. 2000 https://doi.org/10.1109/34.895972
  3. S. Michael, 'Next Generation Web Searches for Visual Content,' IEEE Computer, pp.46-52, Nov. 2000 https://doi.org/10.1109/2.881694
  4. M. Flicker, H. Sawhneyy, W. Niblack, J. Ashley and P. Yanker 'Query by Image and Video Content: The QBIC System,' IEEE Computer Special Issue on Content Based Picture Retrieval System, Vol. 28, pp.23-32, 1995 https://doi.org/10.1109/2.410146
  5. W.Y. Ma and B.S. Manjunath 'Netra: A Tool-box for Navigating Large Image Database,' IEEE Conference on Image Processing, Vol. 1, pp.568-571, 1997
  6. J. Smith and S. Chang 'VisualSEEK: A Fully Automated Content-Based Image Query System,' ACM Multimedia, pp.87-98, 1996
  7. C. Carson, M. Thomas, S. Belongie, J.M. Hellerstein and J. Malik, 'Blobworld: A System for Region-based Image Indexing and Retrieval,' International Conference of Visual Information System, Vol. 3, pp.509-516, 1999
  8. W. Osberger and A.J. Maeder, 'Automatic Identification of Perceptually Important Regions in An Image,' IEEE International Conference on Pattern Recognition, pp.701-704, 1998 https://doi.org/10.1109/ICPR.1998.711240
  9. J. Luo and A. Singhal, 'On Measuring Low Level Saliency in Photographic Images,' IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp.84-89, 2000 https://doi.org/10.1109/CVPR.2000.855803
  10. C. M. Privitera and L.W Stark, 'Algorithms for Defining Visual Regions of Interest: Comparison with Eye Fixations,' IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 22, pp.970-982, Sep. 2000 https://doi.org/10.1109/34.877520
  11. J. Senders, 'Distribution of Attention in Static and Dynamic Scenes,' Proceedings of SPIE Human Vision and Electronic Imaging II, vol. 3016, pp.186-194, Feb. 1997 https://doi.org/10.1117/12.274513
  12. Y. Lu and H. Guo, 'Background Removal in Image Indexing and Retrieval,' Proceedings of 10th International Conference on Image Analysis and Processing, pp.933-938, Sep. 1999 https://doi.org/10.1109/ICIAP.1999.797715
  13. Q. Huang, B. Dom, D. Steels, J. Ashely, and W. Niblack, 'Foreground background segmentation of color images by integration of multiple cues,' International Conference on Image Processing, vol. 1, pp.246-249, 1995 https://doi.org/10.1109/ICIP.1995.529692
  14. T. Tamaki, T. Yamamura, and N. Ohnishi, 'Image Segmentation and Object Extraction Based on Geometric Features of Regions,' SPIE Conference on Visual Communications and Image Processing, vol. 3653, pp.937-945, Jan. 1999 https://doi.org/10.1117/12.334746
  15. J. R. Serra and J. B. Subirana, 'Texture Frame Curves and Regions of Attention Using Adaptive Non-cartesian Networks,' Pattern Recognition, vol. 32, pp.503-515, Mar. 1999 https://doi.org/10.1016/S0031-3203(98)00040-5
  16. L. Itti, C. Koch, and E. Niebur, 'A Model of Saliency-based Visual Attention for Rapid Scene Analysis,' IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.20, No. 11, pp.1254- 1259, 1998 https://doi.org/10.1109/34.730558
  17. A. M. Andrew, 'Another Efficient Algorithm for Convex Hulls in Two Dimensions,' Information Processing Letters, pp. 216-219, 1979 https://doi.org/10.1016/0020-0190(79)90072-3
  18. D. Wang, 'Unsupervised Video Segmentation Based on Watersheds and Temporal Tracking,' IEEE Transaction on Circuits and System for Video Technology, Vol. 2, pp.539-546, 1998 https://doi.org/10.1109/76.718501
  19. Y. Hu, X. Xie, W. Y Ma, L. T. Chia, and D. Rajan, 'Salient Region Detection Using Weighted Feature Maps Based on The Human Visual Attention Model,' Proceedings of the Fifth IEEE Pacific-Rim Corference on Multimedia, Vol. 2, pp.993-1000, 2004