Video Shot Boundary Detection Method Using Modified Artificial Bee Colony and Fast Accelerated Segment Test Algorithm

  • Authors

    • Isra'a Hadi
    • Hikmat Z. Neima
    • . .
  • Shot boundary detection, Artificial Bee Colony, Fast Accelerated Segment Test, Fuzzy Histogram.
  • Over recent years, video has become one of the most important means of communication. This is due to the rapid growth in network bandwidth and device storage capacity. The management of video databases therefore requires efficient video analysis methods. Video shot boundary detection (SBD) is considered an essential step in a wide range of video analysis processes such as video browsing, indexing, retrieval and summarization. Many SBD methods have been proposed; however, accuracy in the detection of shot boundaries is still unsatisfactory. In this paper, an efficient SBD method is proposed. The proposed method consists of two stages: identification and verification. In the identification stage, candidate shot boundaries are identified based on a modified Artificial Bee Colony (ABC) algorithm. In the verification stage, the global and local features of frames are extracted to verify the shot boundaries. Fuzzy histograms are utilized as a global feature while features from Fast Accelerated Segment Test (FAST) corner detection is implemented to extract interest points as a local feature. The proposed method has shown highly accurate results.



  • References

    1. [1] Hannane, R., A. Elboushaki, and K. Afdel. "Efficient video summarization based on motion SIFT-Distribution histogram". IEEE 13th International Conference in Computer Graphics, Imaging and Visualization (CGiV), 2016.

      [2] Koprinska, I. and S. Carrato, "Temporal video segmentation: A survey", 2001, 16(5): p. 477-500.

      [3] Cotsaces, C., N. Nikolaidis, and I. Pitas, "Video shot detection and condensed representation: a review". IEEE signal processing magazine, 2006. 23(2): p. 28-37.

      [4] Boreczky, J.S. and L.A. Rowe, "Comparison of video shot boundary detection techniques", Journal of Electronic Imaging, 1996. 5(2): p. 122-129.

      [5] Karaboga, D., et al., "A comprehensive survey: artificial bee colony (ABC) algorithm and applications", Artificial Intelligence Review, 2014. 42(1): p. 21-57.

      [6] Huo, Y., Y. Wang, and H. Hu, "Effective algorithms for video shot and scene boundaries detection", IEEE 15th International Conference on Computer and Information Science (ICIS), 2016 IEEE/ACIS.

      [7] He, Z.-a., et al., "A modified artificial bee colony algorithm based on search space division and disruptive selection strategy", Mathematical Problems in Engineering, 2014.

      [8] Hadi, I. and M. Sabah, "Upgrade Video Tracking Technique Using Enhanced Hybrid Cat Swarm Optimization Based on Multi Target Model and Accumulated Histogram", Journal of Computational and Theoretical Nanoscience, 2015, 12(11): p. 4017-4027.

      [9] Mao, W., H.-y. Lan, and H.-r. Li, "A new modified artificial bee colony algorithm with exponential function adaptive steps. Computational intelligence and neuroscience", 2016: p. 23.

      [10] Li, J.-q., Q.-k. Pan, and P.-y. Duan, "An improved artificial bee colony algorithm for solving hybrid flexible flowshop with dynamic operation skipping", IEEE transactions on cybernetics, 2016. 46(6): p. 1311-1324.[11] Zhu, G. and S. Kwong, "Gbest-guided artificial bee colony algorithm for numerical function optimization", Applied mathematics and computation, 2010. 217(7): p. 3166-3173.[12] Karaboga, D. and B. Gorkemli, "A quick artificial bee colony-qABC-algorithm for optimization problems", IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA), 2012.[13] Jia, D.-L., S.-X. Qu, and L.-Y. Li., "A Multi-swarm Artificial Bee Colony Algorithm for Dynamic Optimization Problems", IEEE International Conference on Information System and Artificial Intelligence (ISAI), 2016.[14] Yi-bo, L. and L. Jun-Jun, "Harris corner detection algorithm based on improved contourlet transform", Procedia Engineering, 2011. 15: p. 2239-2243.[15] Rosten, E. and T. Drummond," Fusing points and lines for high performance tracking", Tenth IEEE International Conference on Computer Vision, ICCV 2005.[16] Acharya, T. and A.K. Ray, "Image processing: principles and applications", 2005: John Wiley & Sons.[17] Gera, Z. and J. Dombi, "Fuzzy reasoning models and fuzzy truth value based inference". Department of Computer, 2009.[18] Xu, J., L. Song, and R. Xie, "Shot boundary detection using convolutional neural networks". IEEE Conference on Visual Communications and Image Processing (VCIP), 2016.[19] Gargi, U., R. Kasturi, and S.H. Strayer," Performance characterization of video-shot-change detection methods", IEEE transactions on circuits and systems for video technology, 2000. 10(1): p. 1-13.[20] Bae, T.M., S.H. Jin, and Y.M. Ro, "Video segmentation using hidden Markov model with multimodal features", International Conference on Image and Video Retrieval, 2004: Springer.[21] Cooper, M., T. Liu, and E. Rieffel, "Video segmentation via temporal pattern classification", IEEE Transactions on Multimedia, 2007. 9(3): p. 610-618.[22] Li, Y.-N., Z.-M. Lu, and X.-M. Niu, "Fast video shot boundary detection framework employing pre-processing techniques", IET image processing, 2009. 3(3): p. 121-134.[23] Mohanta, P.P., S.K. Saha, and B. Chanda," A model-based shot boundary detection technique using frame transition parameters", IEEE Transactions on Multimedia, 2012. 14(1): p. 223-233.[24] Birinci, M. and S. Kiranyaz, "A perceptual scheme for fully automatic video shot boundary detection", signal processing: image communication, 2014. 29(3): p. 410-423.[25] Tippaya, S., et al., "Video shot boundary detection based on candidate segment selection and transition pattern analysis", IEEE International Conference on Digital Signal Processing (DSP), 2015.[26] Fanan, N.G. and A.S. Khobragade, "A fast robust technique for video shot boundary detection", IEEE Online International Conference on Green Engineering and Technologies (IC-GET), 2016.[27] Du, K.-L. and M.N. Swamy, "Neural networks and statistical learning", 2013: Springer Science & Business Media.
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  • How to Cite

    Hadi, I., Z. Neima, H., & ., . (2018). Video Shot Boundary Detection Method Using Modified Artificial Bee Colony and Fast Accelerated Segment Test Algorithm. International Journal of Engineering & Technology, 7(4.19), 636-641.