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Efficient Video Retrieval Scheme with Luminance Projection Model

휘도투시모델을 적용한 효율적인 비디오 검색기법

  • Kim, Sang Hyun (School of Convergence & Fusion System Engineering, Kyungpook National University)
  • 김상현 (경북대학교 융복합시스템공학부)
  • Received : 2015.10.16
  • Accepted : 2015.12.04
  • Published : 2015.12.31

Abstract

A number of video indexing and retrieval algorithms have been proposed to manage large video databases efficiently. The video similarity measure is one of most important technical factor for video content management system. In this paper, we propose the luminance characteristics model to measure the video similarity efficiently. Most algorithms for video indexing have been commonly used histograms, edges, or motion features, whereas in this paper, the proposed algorithm is employed an efficient similarity measure using the luminance projection. To index the video sequences effectively and to reduce the computational complexity, we calculate video similarity using the key frames extracted by the cumulative measure, and compare the set of key frames using the modified Hausdorff distance. Experimental results show that the proposed luminance projection model yields the remarkable improved accuracy and performance than the conventional algorithm such as the histogram comparison method, with the low computational complexity.

대용량 비디오 데이터베이스들을 효율적으로 관리하기 위해 많은 비디오 색인 및 검색 알고리즘들이 제안되고 있다. 비디오 콘텐츠 관리 시스템에서 비디오 유사도 측정방법은 가장 중요한 기술적 요소 중 하나이다. 본 논문에서는 비디오 유사도를 효율적으로 측정하기 위해 휘도특성 모델을 제안한다. 비디오 색인에 관한 대부분의 알고리즘들이 공통적으로 히스토그램, 윤곽선, 움직임 특성을 사용한 반면 본 논문에서 제안한 알고리즘은 휘도투시를 사용한 효율적인 유사도 측정법을 적용하였다. 비디오 시퀀스의 효율적인 색인과 계산량 감소를 위해 누적된 유사도에 의해 추출된 키프레임 들을 이용한 비디오 유사도를 계산하고 수정된 하우스도르프 거리를 사용하여 키프레임 묶음들을 비교하였다. 실험결과 제안한 휘도투시 모델이 적은 계산량으로 기존의 히스토그램 비교법을 사용한 알고리즘에 비해 현저히 향상된 정확도 및 성능을 보였다.

Keywords

References

  1. X. Wen, L. Shao, W. Fang, and Y. Xue, "Efficient feature selection and classification for vehicle detection," IEEE Trans. Circuits and Systems for Video Technology, vol. 25, no. 3, pp. 508-517, Mar. 2015. https://doi.org/10.1109/TCSVT.2014.2358031
  2. G. Luis, D. Tuia, G. Moser, C. Gustau, "Multimodal classification of remote sensing images: A review and future directions," Proc. of IEEE, vol. 103, no. 9, pp. 1560-1584, Sep. 2015. https://doi.org/10.1109/JPROC.2015.2449668
  3. Z. A. Jaffery and A. K. Dubey, "Architecture of noninvasive real time visual monitoring system for dial type measuring instrument," IEEE Sensors Journal, vol. 13, no. 4, pp. 1236-1244, Apr. 2013. https://doi.org/10.1109/JSEN.2012.2231940
  4. Y. Yang, Z. Zha, Y. Gao, X. Zhu, and T. Chua, "Exploiting web Images for semantic video indexing via robust sample-specific loss," IEEE Trans. Multimedia, vol. 16, no. 6, pp. 1677-1689, Aug. 2014. https://doi.org/10.1109/TMM.2014.2323014
  5. V. T. Chasanis, A. C. Likas, and N. P. Galatsanos, "Scene detection in video using shot clustering and sequence alignment," IEEE Trans. Multimedia, vol. 11, no. 1, pp. 89-100, Jan. 2009. https://doi.org/10.1109/TMM.2008.2008924
  6. J. Geng, Z. Miao, and X.-P. Zhang, "Efficient heuristic methods for multimodal fusion and concept fusion in video concept detection," IEEE Trans. Multimedia, vol. 17, no. 4, pp. 498-511, Apr. 2015. https://doi.org/10.1109/TMM.2015.2398195
  7. H Yan, K. Paynabar, and H. Shi, "Image-based process monitoring using low-rank tensor decomposition," IEEE Trans. Automation Science and Engineering, vol. 12, no. 1, pp. 216-227, Jan. 2015. https://doi.org/10.1109/TASE.2014.2327029
  8. Y. Yin, Y. Yu, and R. Zimmermann, "On generating content-oriented geo features for sensor-rich outdoor video search," IEEE Trans. Multimedia, vol. 17, no. 10, pp. 1760-1772, Oct. 2015. https://doi.org/10.1109/TMM.2015.2458042