DOI QR코드

DOI QR Code

Image Feature Point Selection Method Using Nearest Neighbor Distance Ratio Matching

최인접 거리 비율 정합을 이용한 영상 특징점 선택 방법

  • Received : 2012.09.05
  • Published : 2012.12.25

Abstract

In this paper, we propose a feature point selection method for MPEG CDVS CE-7 which is processing on International Standard task. Among a large number of extracted feature points, more important feature points which is used in image matching should be selected for the compactness of image descriptor. The proposed method is that remove the feature point in the extraction phase which is filtered by nearest neighbor distance ratio matching in the matching phase. We can avoid the waste of the feature point and employ additional feature points by the proposed method. The experimental results show that our proposed method can obtain true positive rate improvement about 2.3% in pair-wise matching test compared with Test Model.

본 논문에서는 현재 진행 중인 MPEG(Motion Picture Experts Group, ISO/IEC JTC1 SC29 WG11)의 표준화 작업 중 CDVS(Compact Descriptor for Visual Search)의 CE-7(Core Experiment)인 특징점 선택에 대한 방법을 제안한다. 서술자의 경량화를 위해서는 영상으로부터 추출된 많은 수의 특징점들 중에서 영상 정합에 사용될 중요한 특징점들을 선택해야 한다. 본 논문에서는 최 인접 거리 비율 정합(Nearest Neighbor distance ratio matching) 방법에 의해 영상 정합 단계에서 사용되지 않고 버려지는 특징점들을 미리 추출 단에서 제거하는 방법 제안하였다. 제안된 방법을 통하여 적은 비트 전송률을 요하는 시스템에서 특징점의 낭비를 피할 수 있고 결과적으로 추가적인 특징점을 사용할 수 있으므로 전체적인 성능 향상을 얻을 수 있었다. 제안된 알고리즘을 통하여 Pair-wise 정합 실험에서 기존의 Test Model 대비 최고 2.3%의 성공율(True positive rate)의 향상을 보였다.

Keywords

References

  1. B. Girod, V. Chandrasekhar, R. Grzeszczuk and Y. A. Reznik, "Mobile Visual Search: Architectures, Technologies, and the Emerging MPEG Standard," Multimedia, IEEE, vol. 18, pp. 86-94, 2011. https://doi.org/10.1109/MMUL.2011.48
  2. ISO/IEC JTC1/SC29/WG11, "Call for Proposals for Compact Descriptors for Visual Search," N12201, Jul., 2011.
  3. ISO/IEC JTC1/SC29/WG11, "Description of Core Experiments on Compact Descriptors for Visual Search," N12930, Jul., 2012.
  4. ISO/IEC JTC1/SC29/WG11, "Test Model 1: Compact Descriptors for Visual Search," N12550, Feb 2012.
  5. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  6. S. Tsai, D. Chen, G. Takacs, V. Chandrasekhar, J. P. Singh, and B. Girod, "Location coding for mobile image retrieval," International Mobile Multimedia Communications Conference, Sep., 2009.
  7. S. Lepsoy, G. Francini, G. Cordara and P. P. B. de Gusmao, "Statistical modelling of outliers for fast visual search," in Multimedia and Expo (ICME), 2011 IEEE International Conference on, pp. 1-6, 2011.
  8. S. Tsai, D. Chen, G. Takacs, V. Chandrasekhar, R. Vedantham, R. Grzeszczuk and B. Girod, "Fast geometric re-ranking for image-based retrieval," in Image Processing (ICIP), 17th IEEE International Conference, pp. 1029-1032, 2010
  9. ISO/IEC JTC1/SC29/WG11, "Evaluation Framework for Compact Descriptors for Visual Search," N12202, Jul., 2011.
  10. Test Model 1.5, available for download at https://pacific.tilab.com/gf/project/cdvs/frs/
  11. A. Vedaldi and B. Fulkerson, "VLFeat: An Open and Portable Library of Computer Vision Algorithms," 2008, http://www.vlfeat.org