The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
권호 표지
DOI QR코드

DOI QR Code

Automatic Extraction of Major Object in the Image based on Image Composition

영상구도에 근거한 영상내의 주요객체 자동추출 기법

  • 강선도 (고려대학교 정보경영공학부) ;
  • 유헌우 (연세대학교 컴퓨터과학과) ;
  • 신영근 (고려대학교 정보경영공학부) ;
  • 장동식 (고려대학교 정보경영공학부)
  • Published : 2008.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

A new algorithm for automatic extraction of interesting objects is proposed in this paper. The proposed algorithm can be summarized in two steps. First, segmentation of color image that split interesting objects and backgrounds is performed. According to the research stating, 'Humans perceive things by contracting color into three to four essential colors,' a color image is segmented into three regions utilizing k-mean algorithm, followed by annexing the regions when the similarities of them exceeds the critical value based on the calculation of degrees in the histogram similarity, Second, identifying the interesting objects out of the segmented image, partitioned by the image composition theory, is performed. To have a good picture, it is important to adjust positions of interesting objects according to picture composition. Extracting objects is a retro-deduction process using a weighted mask designed upon the triangular composition of picture. To prove the quality of the proposed method, experiments are performed over four hundreds images as well as comparison with recently proposed KMCC and GBIS methods.

본 논문에서는 새로운 주요객체 자동추출 알고리즘을 제안한다. 제안된 알고리즘은 크게 2단계 과정으로 요약될 수 있다. 1단계로 객체와 배경을 분리하는 영상분할 작업을 수행한다. 우선적으로 '인간은 3또는 4개의 주요 색상으로 축약하여 사물을 인식한다'는 연구 결과에 따라 K-means 알고리즘을 이용하여 3구역으로 분할하고, 분할된 영상 간 히스토그램 유사도를 계산하여 임계값 이상으로 유사하면 병합하는 과정을 수행한다. 2단계로 영상구도에 근거해 분할된 영상 중에 객체라고 지정하는 작업을 수행한다. 사람이 사진을 잘 찍기 위해서는 '주요객체의 위치를 영상구도에 맞추어 촬영하는 것이 바람직하다는 사실'에 근거하여 삼각구도를 바탕으로 가중치 마스크를 설계하여 객체위치를 역 추정하였다. 제안된 방법의 우수성을 보이기 위해 약 400개의 영상에 대해 실험하였으며, 최근에 발표된 KMCC, GBIS방법과도 비교하였다.

Keywords

References

  1. J. Z. Wang, J. Li, R. M. Gray, and G. Wiederhold, Unsupervised Multiresolution Segmentation for Images with Low Depth of Field. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.23, No.1, pp.85-90, 2001. https://doi.org/10.1109/34.899949
  2. R. M. Haralick and L. G. Shapiro. Survey: Image Segmentation Techniques. Computer Vision, Graphics and Image Processing, Vol.29, No.1, pp.100-132, 1985. https://doi.org/10.1016/S0734-189X(85)90153-7
  3. P. K. Sahoo, S. Soltani, and A. K. C. Wong, A Survey of Thresholding Techniques, Computer Vision, Graphics and Image Processing, Vol.41, No.2, pp.233-260, 1988. https://doi.org/10.1016/0734-189X(88)90022-9
  4. S. D. Kang, H. W. Yoo, and D. S. Jang, Color Image Segmentation Based on the Normal Distribution and the Dynamic Thresholding. Lecture Notes in Computer Science, Vol.4705, pp.372-384, 2007. https://doi.org/10.1007/978-3-540-74472-6_30
  5. Q. Gao, Extracting Object Silhouettes by Perceptual Edge Tracking. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Vol.3, pp.2450-2454, 1997. https://doi.org/10.1109/ICSMC.1997.635296
  6. S. Mahamud, L. R. Williams, K. K. Thornber, and K. Xu, Segmentation of Multiple Salient Closed Contours from Real Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.25, No.4, pp.433-444, 2003. https://doi.org/10.1109/TPAMI.2003.1190570
  7. C. Kim, Segmenting a Low-depth-of-field Image Using Morphological Filters and Region Merging, IEEE Transactions on Image Processing, Vol.14, No.10, pp.1503-1511, 2005. https://doi.org/10.1109/TIP.2005.846030
  8. C. S. Won, K. Pyun, and R. M. Gray, Automatic Object Segmentation in Images with Low Depth of Field. In Proceedings of IEEE International Conference on Image Processing, Vol.3, pp.805-808, 2002. https://doi.org/10.1109/ICIP.2002.1039094
  9. Y. Lu and H. Guo, Background Removal in Image indexing and Retrieval. In Proceedings of International Conference on Image Analysis and Processing, Vol.1, pp.933-938, 1999. https://doi.org/10.1109/ICIAP.1999.797715
  10. A. Blake, C. Rother, M. Brown, P. Perez, and P. Torr. Interactive Image Segmentation Using an Adaptive GMMRF Model. In Proceedings of European Conference on Computer Vision (ECCV2004), Vol.3021, pp.428-441, 2004.
  11. C. Rother, V. Kolmogorov, and A. Blake. GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts. In Proceedings of ACM Siggraph Conference, Vol.23, pp.309-314, 2004. https://doi.org/10.1145/1015706.1015720
  12. Y. Gaobo and Y. Shengfa, Modified Intelligent Scissors and Adaptive Frame Skipping for Video Object Segmentation, Real-time Imaging, Vol.11, No.4, pp.310-322, 2005. https://doi.org/10.1016/j.rti.2005.06.005
  13. A. Mojsilovic', J. Kova'cevic', J. Hu, R. J. Safranek, and S. K. Ganapathy. Matching and Retrieval Based on the Vocabulary and Grammar of Color Patterns. IEEE Transactions on Image Processing, Vol.1, No.1, pp.38-54, 2000. https://doi.org/10.1109/83.817597
  14. I. Biederman, Human Image Understanding: Recent Research and Theory. Computer Vision, Graphics, and Image Processing, Vol.32, No.1, pp.29-73, 1985. https://doi.org/10.1016/0734-189X(85)90002-7
  15. Y. C.Ha. Nature Picture and Human. Donga Press, 2002.
  16. P. F. Felzenszwalb and D. P. Huttenlocher. Efficient Graph- Based Image Segmentation. International Journal of Computer Vision, Vol.59, No.2, pp.167-181, 2004. https://doi.org/10.1023/B:VISI.0000022288.19776.77
  17. V. Mezaris, I. Kompatsiaris, and M. G. Strintzis. Still Image Segmentation Tools for Object-based Multimedia Applications. InternationalJournal of Pattern Recognition and Artificial Intelligence, Vol.18, No.4, pp.701-725, 2004. https://doi.org/10.1142/S0218001404003393