Abstract
In this paper, we propose a topical object discovery method in commerical video. This method utilizes the objectness measure to generate the object candidates from the key-frames of the video. Then a sparse coding method is developed to discover the most topical object. Such a method can provide ranked results and therefore we can easily select the most topical object. The experimental validation on 10 videos shows that the sparse coding method performs better than existing topic mining methods.
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Wang, H., Zhao, G.: Visual pattern discovery in image and video data: a brief survey. WIREs Data Mining Knowl. Discov. 4, 24–37 (2014), doi:10.1002/widm.1110
Yuan, J., Zhao, G., Fu, Y., Li, Z., Katsaggelos, A.K., Wu, Y.: Discovering Thematic Objects in Image Collections and Videos. IEEE Transactions on Image Processing 21(4) (2012)
Russell, B. C., Efros, A. A., Sivic, J., Freeman, W.T., Zisserman, A.: Using Multiple Segmentation to Discover Objects and Their Extent in Image Collections. In Proc. of Computer Vision and Pattern Recognition (CVPR) (2006)
Zhao, G., Yuan, J., Hua G.: Topical Video Object Discovery from Key Frames by Modeling Word Co-occurrence Prior. In Proc. of Computer Vision and Pattern Recognition (CVPR) (2013)
Tang, J., Lewis, P.H.: Non-negative Matrix Factorisation for Object Class Dis-covery and Image Auto-annotation. In:Proc. of the 8th ACM International Conference on Image and Video Retrieval (CIVR)(2008)
Zhao, G., Yuan, J.: Discovering Thematic Patterns in Videos via Cohesive Sub-graph Mining. In: 11th IEEE International Conference on Data Mining (2011)
Alexe, B., Deselaers, T., Ferrari, V.: Measuring the Objectness of Image Win-dows. IEEETransactions on Pattern Analysis and Machine Intelligence (TPAMI) (2012)
Lowe, D.: Object recognition from local scale-invariant features. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1150–1157 (1999)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Graph-Based Image Segmentation. IJCV 59(2), 167–181 (2014)
Nie, F., Huang, H., Cai, X., Ding, C.: Efficient and robust feature selection via joint l2,1 norm minimization. In: Proc. of Advances in Neural Information Processing Systems (NIPS), pp. 1–9 (2010)
Cong, Y., Yuan, J., Liu, J.: Abnormal Event Detection in Crowed Scenes using Sparse Representation. Pattern Recognition 46(7), 1851–1864 (2013)
Cong, Y., Yuan, J., Luo, J.: Towards scalable summarization of consumer videos via sparse dictionary selection. IEEE Trans. on Multimedia 14(1), 66–75 (2012)
Elhamifar, E., Sapiro, G., Vidal, R.: See all by looking at a few: Sparse modeling for finding representive objects. In Proc. of Computer Vision and Pattern Recognition (CVPR), pp. 1600–1607 (2012)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latentdirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
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Liu, Y., Liu, H., Sun, F. (2014). Discovery of the Topical Object in Commercial Video: A Sparse Coding Method. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_26
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DOI: https://doi.org/10.1007/978-3-662-45643-9_26
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