Abstract
We present a surprisingly easy-to-use video browser for helping users to pinpoint a specific video shot in mind, within a long video. At each interactive iteration, the only user effort required is to click 1 shot, which most visually relates to the user’s mental target, out of 8 displayed shots. Then, the system updates the browsing model and display another 8 shots for the next iteration. The proposed system is underpinned by a theoretically-sound Bayesian framework that maintains the probabilities of all the video shots segmented from the long video. This framework guarantees that we can find the target shot out of around 1-h video within 3–5 iterations. We believe that our system will perform well in the Video Broswer Showdown game of MMM 2016.
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Arandjelovic, R., Zisserman, A.: All about VLAD. In: CVPR (2013)
Ferecatu, M., Geman, D.: A statistical framework for image category search from a mental picture. TPAMI 31(6), 1087–1101 (2009)
Jégou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., Schmid, C.: Aggregating local image descriptors into compact codes. TPAMI 34(9), 1704–1716 (2012)
Jia, Y.: Caffe: an open source convolutional architecture for fast feature embedding (2013). http://caffe.berkeleyvision.org
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
Over, P., Awad, G., Michel, M., Fiscus, J., Sanders, G., Shaw, B., Kraaij, W., Smeaton, A.F., Quenot, G.: Trecvid 2012 - an overview of the goals, tasks, data, evaluation mechanisms and metrics. In: TRECVID (2012)
Schoeffmann, K.: A user-centric media retrieval competition: the video browser showdown 2012–2014. IEEE MultiMedia 21, 8–13 (2014)
Schoeffmann, K., Ahlström, D., Bailer, W., Cobârzan, C., Hopfgartner, F., McGuinness, K., Gurrin, C., Frisson, C., Le, D.-D., Del Fabro, M., et al.: The video browser showdown: a live evaluation of interactive video search tools. IJMIR 3(2), 113–127 (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
Xu, Z., Yang, Y., Hauptmann, A.G.: A discriminative cnn video representation for event detection (2014). arXiv preprint arXiv:1411.4006
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© 2016 Springer International Publishing Switzerland
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He, J., Shang, X., Zhang, H., Chua, TS. (2016). Mental Visual Browsing. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_44
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DOI: https://doi.org/10.1007/978-3-319-27674-8_44
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