Abstract
Video shot boundary detection (SBD) is a basic work of content-based video retrieval and analysis. Various SBD methods have been proposed; however, there exist limitations in the complexity of boundary detection process. In this paper, a simple yet efficient SBD method is proposed, and the aim here is to speed up the boundary detection and simplify the detection process without loss of detection recall and accuracy. In our proposed model, we mainly use a top-down zoom rule, the image color feature, and local descriptors and combine a kind of motion area extraction algorithm to achieve shot boundary detection. Firstly, we select candidate transition segments via color histogram and the speeded-up robust features. Then, we perform cut transition detection through uneven slice matching, pixel difference, and color histogram. Finally, we perform gradual transition detection by the motion area extraction, scale-invariant feature transform, and even slice matching. The experiment is evaluated on the TRECVid2001 and the TRECVid2007 video datasets, and the experimental results show that our proposed method improves the recall, accuracy, and the detection speed, compared with some other related SBD methods.
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References
Shekar, B., Kumari, M.S., Holla, R.: Shot boundary detection algorithm based on color texture moments. In: International Conference on Advances in Communication, Network, and Computing, pp. 591–594. Springer (2011)
Jiang, X., Sun, T., Liu, J., Chao, J., Zhang, W.: An adaptive video shot segmentation scheme based on dual-detection model. Neurocomputing 116, 102–111 (2013)
Li, Z., Liu, X., Zhang, S.: Shot boundary detection based on multilevel difference of colour histograms. In: 2016 First International Conference on Multimedia and Image Processing, pp. 15–22. IEEE (2016)
Tippaya, S., Sitjongsataporn, S., Tan, T., Khan, M.M., Chamnongthai, K.J.I.A.: Multi-modal visual features-based video shot boundary detection. IEEE Access 5, 12563–12575 (2017)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Bay, H., Tuytelaars, T., Van Gool, L.: Surf: speeded up robust features. In: European Conference on Computer Vision, pp. 404–417. Springer (2006)
Birinci, M., Kiranyaz, S.: A perceptual scheme for fully automatic video shot boundary detection. Signal Process. Image Commun. 29(3), 410–423 (2014)
Mishra, R., Singhai, S., Sharma, M.: Video shot boundary detection using dual-tree complex wavelet transform. In: 2013 3rd International Advance Computing Conference, pp. 1201–1206. IEEE (2013)
Hannane, R., Elboushaki, A., Afdel, K., Naghabhushan, P., Javed, M.: An efficient method for video shot boundary detection and keyframe extraction using SIFT-point distribution histogram. Int. J. Multimed. Inf. Retr. 5(2), 89–104 (2016)
Shen, R.-K., Lin, Y.-N., Juang, T.T.-Y., Shen, V.R., Lim, S.Y.: Automatic detection of video shot boundary in social media using a hybrid approach of HLFPN and keypoint matching. IEEE Trans. Comput. Soc. Syst. 5(1), 210–219 (2017)
Youssef, B., Fedwa, E., Driss, A., Ahmed, S.J.C.V., Understanding, I.: Shot boundary detection via adaptive low rank and svd-updating. Comput. Vis. Image Underst. 161, 20–28 (2017)
Kavitha, J., Jansi Rani, P.A., Sowmyayani, S.: Wavelet-based feature vector for shot boundary detection. Int. J. Image Graph. 17(01), 1750002 (2017). https://doi.org/10.1142/S0219467817500024
küçüktunç, O., Güdükbay, U., Ulusoy, Ö.: Fuzzy color histogram-based video segmentation. Comput. Vis. Image Underst. 114(1), 125–134 (2010)
Fan, J., Zhou, S., Siddique, M.A.: Fuzzy color distribution chart-based shot boundary detection. Multimed. Tools Appl. 76(7), 10169–10190 (2017)
Chakraborty, B., Bhattacharyya, S., Chakraborty, S.: An unsupervised approach to video shot boundary detection using fuzzy membership correlation measure. In: 2015 Fifth International Conference on Communication Systems and Network Technologies, pp. 1136–1141. IEEE (2015)
Bhaumik, H., Bhattacharyya, S., Chakraborty, S.: A vague set approach for identifying shot transition in videos using multiple feature amalgamation. Appl. Soft Comput. 75, 633–651 (2019)
Thounaojam, D.M., Khelchandra, T., Singh, K.M., Roy, S.: A genetic algorithm and fuzzy logic approach for video shot boundary detection. Comput. Intell. Neurosci. 2016(1), 14 (2016)
Yazdi, M., Fani, M.: Shot boundary detection with effective prediction of transitions’ positions and spans by use of classifiers and adaptive thresholds. In: 2016 24th Iranian Conference on Electrical Engineering (ICEE), pp. 167–172. IEEE (2016)
Mondal, J., Kundu, M.K., Das, S., Chowdhury, M.: Video shot boundary detection using multiscale geometric analysis of nsct and least squares support vector machine. Multimed. Tools Appl. 77(7), 8139–8161 (2018)
Xu, J., Song, L., Xie, R.: Shot boundary detection using convolutional neural networks. In: 2016 Visual Communications and Image Processing (VCIP), pp. 1–4. IEEE (2016)
Juan, L., Gwon, O.: A comparison of sift, pca-sift and surf. Int. J. Image Process. 8(3), 169–176 (2007)
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Zhou, S., Wu, X., Qi, Y. et al. Video shot boundary detection based on multi-level features collaboration. SIViP 15, 627–635 (2021). https://doi.org/10.1007/s11760-020-01785-2
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DOI: https://doi.org/10.1007/s11760-020-01785-2