Abstract
Content Based Video Retrieval (CBVR) is an approach for redeeming 3D videos from several video sources such as YouTube, Viki etc. Various modern video retrieval applications are revealed by using approaches like key frame selection, features extraction, similarity matching, etc. We propound a new framework that gathers key frame selection, feature extraction, denoising and similarity matching for effective retrieval of videos from 3D images. We initiate Relative Entropy based Fast Key Frame Selection (REFKFS) for nominating optimal key frames for a video. BM3D filter with Bayesian threshold (BB) is proposed for decreasing white Gaussian noises on 3D key frames. We extract texture features, shape features, motion features and color features from the key frames. For extricating shape features, we prefer moments of objects and regions. After extricating features, we propose similarity matching process which is established from Multi-Featured Light-weight (MFLW) matching scheme for successful retrieval of videos with 3D images. Here we qualify Hadoop environment for handling massive sized database on video retrieval. Our experimental outcome includes larger database and furnish efficacious result as 98% of accuracy obtained for overall proposed framework.
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References
Hu, W., Xie, N., Li, L., Zeng, X., Maybank, S.: A survey on visual content-based video indexing and retrieval. IEEE Trans. Syst. Man Cybern. 41(6), 797–819 (2011)
Lew, M.S., Sebe, N., Eakins, J.P.: Challenges of Image and Video Retrieval, vol. 2383, pp. 1–6. Springer, Heidelberg (2002)
Jain, P., Gyanchandani, M., Khare, N., Singh, D.P., Rajesh, L.: A survey on big data privacy using Hadoop architecture. Int. J. Comput. Sci. Netw. Secur. 17(2), 148–155 (2017)
Wang, H., Zhu, F., Xiao, B., Wang, L., Jiang, Y.-G.: GPU-based MapReduce for large-scale near-duplicate video retrieval. In: Multimedia Tools Applications, pp. 1–20. Springer, Heidelberg (2014)
Zhang, Y., Chen, X., Lin, L., Xia, C., Zou, D.: High-level representation sketch for video event retrieval. In: Science China Information Sciences, vol. 59, pp. 1–15. Springer, Heidelberg (2016)
Lin, T.-C., Yang, M.-C., Tsai, C.-Y., Wang, Y.-C.F.: Query-adaptive multiple instance learning for video instance retrieval. IEEE Trans. Image Process. 24(4), 1330–1340 (2015)
Fridman, L., Reimer, R.: Semi-automated annotation of discrete states in large video datasets. Association for the Advancement of Artificial Intelligence, 1–6 (2016)
Sabokrou, M., Fayyaz, M., Fathy, M., Klette, R.: Deep-cascade: cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes. IEEE Trans. Image Process. 1–13 (2017)
Brindha, N., Visalakshi, P.: Bridging semantic gap between high-level and low-level features in content-based video retrieval using multi-stage ESN–SVM classifier. In: Indian Academy of Sciences, pp. 1–10. Springer, Heidelberg (2016)
Choi, M.-K., Wang, Z., Lee, H.-G., Lee, S.-C.: A bag-of-regions representation for video classification. In: Multimedia Tools Application, pp. 1–20. Springer (2015)
Nagaraja, G.S., Rajashekara Murthy, S., Deepak T.S.: Content based video retrieval using support vector machine classification. In: IEEE Conference on Applied and Theoretical Computing and Communication Technology, pp. 821–827 (2015)
Huangfu, Z.-M., Zhang, S.-S., Yan, L.-H.: A method of 3D CAD model retrieval based on spatial bag of words. In: Multimedia Tools Application, pp. 1–29. Springer, Heidelberg (2016)
Zheng, R., Yao, C., Jin, H., Zhu, L., Zhang, Q., Deng, W.: Parallel key frame extraction for surveillance video service in a smart city. PLoS ONE 1–8 (2015)
Lu, G., Zhou, Y., Li, X., Yan, P.: Unsupervised, efficient and scalable key-frame selection for automatic summarization of surveillance videos. In: Multimedia Tools Application, pp. 1–23. Springer, Heidelberg (2016)
Nga, D.H., Yanai, K.: Automatic collection of web video shots corresponding to specific actions using web images. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 15–20 (2012)
Mohamadzadeh, S., Farsi, H.: Content based video retrieval based on HDWT and sparse representation. Image Anal. Stereol. 35, 67– 81 (2016)
Sajjad, M., Mehmood, I., Baik, S.W.: Sparse representations-based super-resolution of key-frames extracted from frames-sequences generated by a visual sensor network. Sensors 14, 3652–3674 (2014)
Khalid, M., P. Sajith Sethu, P.S., Sethunadh, R.: Video denoising using surfacelet transform. In: IEEE International Conference on Recent Trends in Electronics Information Communication Technology, pp. 1502–1506 (2016)
Papari, G., Idowu, N., Varslot, T.: Fast bilateral filtering for denoising large 3D images. IEEE Trans. Image Process. 1–12 (2016)
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Ranjith Kumar, C.M., Suguna, S. (2018). A Powerful and Lightweight 3D Video Retrieval Using 3D Images Over Hadoop MapReduce. In: Hemanth, D., Smys, S. (eds) Computational Vision and Bio Inspired Computing . Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_65
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DOI: https://doi.org/10.1007/978-3-319-71767-8_65
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