Advanced SearchSearch Tips
Efficient Video Retrieval Scheme with Luminance Projection Model
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Efficient Video Retrieval Scheme with Luminance Projection Model
Kim, Sang Hyun;
  PDF(new window)
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.
luminance projection;video similarity measure;video indexing;key frame extraction;modified Hausdorff distance;video retrieval;
 Cited by
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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)