Abstract
In this paper we describe a method for efficient video rushes segmentation. Video rushes are unedited video footage and contain many repetitive information, since the same scene is taken many times until the desired result is produced. Color histograms have difficulty in capturing the scene changes in rushes videos. In the herein approach shot frames are represented by semantic feature vectors extracted from existing semantic concept detectors. Moreover, each shot keyframe is represented by the mean of the semantic feature vectors of its neighborhood, defined as the frames that fall inside a window centered at the keyframe. In this way, if a concept exists in most of the frames of a keyframe’s neighborhood, then with high probability it exists on the corresponding keyframe. By comparing consecutive pairs of shots we seek to find changes in groups of similar shots. To improve the performance of our algorithm, we employ a face and body detection algorithm to eliminate false boundaries detected between similar shots. Numerical experiments on TRECVID rushes videos show that our method efficiently segments rushes videos by detecting groups of similar shots.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dumont, E., Merialdo, B.: Rushes video parsing using video sequence alignment. In: Seventh International Workshop on Content-Based Multimedia Indexing, CBMI 2009, pp. 44–49 (2009)
Ren, J., Jiang, J.: Hierarchical modeling and adaptive clustering for real-time summarization of rush videos. IEEE Transactions on Multimedia 11(5), 906–917 (2009)
Rossi, E., Benini, S., Leonardi, R., Mansencal, B., Benois-Pineau, J.: Clustering of scene repeats for essential rushes preview. In: 10th Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS 2009, pp. 234–237 (2009)
Chasanis, V., Likas, A., Galatsanos, N.: Video rushes summarization using spectral clustering and sequence alignment. In: TVS 2008: Proceedings of the 2nd ACM TRECVid Video Summarization Workshop, Vancouver, British Columbia, Canada, pp. 75–79 (2008)
Chasanis, V., Likas, A., Galatsanos, N.: Scene detection in videos using shot clustering and sequence alignment. IEEE Transactions on Multimedia 11(1), 89–100 (2009)
Jiang, Y.G., Ngo, C.W., Yang, J.: Towards optimal bag-of-features for object categorization and semantic video retrieval. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR 2007, pp. 494–501 (2007)
Zhu, S., Wang, G., Ngo, C.W., Jiang, Y.G.: On the sampling of web images for learning visual concept classifiers. In: Proceeding of the ACM International Conference on Image and Video Retrieval (CIVR 2010), pp. 50–57 (2010)
Smeaton, A.F., Over, P., Kraaij, W.: Evaluation campaigns and trecvid. In: Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval, pp. 321–330 (2006)
Kennedy, L., Hauptmann, A.: Lscom lexicon definitions and annotations version 1.0, dto challenge workshop on large scale concept ontology for multimedia. Technical report, Columbia University (March 2006)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Yang, J., Jiang, Y.G., Hauptmann, A.G., Ngo, C.W.: Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval, MIR 2007, pp. 197–206 (2007)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. 511–518 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Pappa, A., Chasanis, V., Ioannidis, A. (2014). Rushes Video Segmentation Using Semantic Features. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-07064-3_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07063-6
Online ISBN: 978-3-319-07064-3
eBook Packages: Computer ScienceComputer Science (R0)