Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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Scene Recognition Using Local and Global Features

지역적, 전역적 특징을 이용한 환경 인식

  • Received : 2012.04.05
  • Accepted : 2012.05.25
  • Published : 2012.06.05
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Abstract

In this paper, we propose an integrated algorithm for scene recognition, which has been a challenging computer vision problem, with application to mobile robot localization. The proposed scene recognition method utilizes SIFT and visual words as local-level features and GIST as a global-level feature. As local-level and global-level features complement each other, it results in improved performance for scene recognition. This improved algorithm is of low computational complexity and robust to image distortions.

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References

  1. Oliva, A. and Torralba, A., "Modeling the Shape of the Scene : A Holistic Representation of the Spatial Envelope", International Journal of Computer Vision 42 (3), pp. 145-175, 2001. https://doi.org/10.1023/A:1011139631724
  2. C. Siagian and L. Itti, "Rapid Biologically-inspired Scene Classification using Features Shared with Visual Attention", IEEE Trans. Pattern Anal. Mach. Intell. 29 (2), pp. 300-312, 2007. https://doi.org/10.1109/TPAMI.2007.40
  3. Lowe, D. G., "Distinctive Image Features from Scale-invariant Keypoints", International Journal of Computer Vision 60 (2), pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  4. Bay, H., Ess, A. and Tuytelaars, T., Van Gool, L., "Speeded-Up Robust Features(SURF)", Computer Vision and Image Understanding 110 (3), pp. 346-359. 2006.
  5. Van De Sande, K., Gevers and T., Snoek, C., "Evaluating Color Descriptors for Object and Scene Recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (9), Art. No. 5204091, pp. 1582-1596, Sept. 2010. https://doi.org/10.1109/TPAMI.2009.154
  6. Liu, Ce and Yuen, Jenny and Torralba, Antonio and Sivic, Josef and Freeman, William T., "SIFT Flow : Dense Correspondence across Different Scenes", ECCV, 2008.
  7. Lazebnik, S., Schmid, C. and Ponce, J., "Beyond Bags of Features : Spatial Pyramid Matching for Recognizing Natural Scene Categories", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2, Art. No. 1641019, pp. 2169-2178, 2006.
  8. S. Lloyd, "Least Squares Quantization in PCM", IEEE Transactions on Information Theory, Vol. 28, pp. 129-137, 1982. https://doi.org/10.1109/TIT.1982.1056489
  9. B. C. Russell, A. Torralba, K. P. Murphy, W. T. Freeman, "LabelMe : A Database and Web-based Tool for Image Annotation", International Journal of Computer Vision, pages 157-173, Volume 77, Numbers 1-3, May, 2008. https://doi.org/10.1007/s11263-007-0090-8
  10. A. Vedaldi, A. Zisserman, "Efficient Additive Kernels via Explicit Feature Maps", IEEE Conference on Computer Vision and Pattern Recognition, 2010.