8. Outlook and Conclusion

8. Outlook and Conclusion More features Beside considering a wider field of video content, it would be helpful to extract even more features from the incoming video data. By extracting features directly from MPEG compressed video data, things could further been fastened up. Additionally some scientists have already proven that MPEG features can be useful to detect video segments ([Lee et al., 2000]). Furthermore it turned out that including audio data, as long as it comes along with the video data, can be useful as well. Daxenberger’s approach for audio segmentation [Daxenberger, 2007] shows, how good segmentation on audio data can be solely. Using multi-modal approaches, as described in chapter 2.5.1, will even improve the segmentation quality. Finally, EPG data would also deliver a bunch of new and interesting features. When the amount of extractable features rises, the selection of valuable features has to be repeated as well. Beside focusing on features that get selected by certain machine learning algorithms, a direct feature selection is also possible using the RapidMiner feature selection extension by Schowe [Schowe, 2011]. This could also be tried out in future. More possible tags Last but not least, the presented use cases do not at all cover all possible tags that would be useful within the ViSTA-TV project. As mentioned in chapter 2.4, a further emphasis should be put on the extraction of more tags. Good examples are ”advertise” vs. ”no advertise”, ”photographic flashlight”, or the names of actors. Conclusion This work shows that a huge amount of reasonable features for video segmentation and tagging can be extracted on live data in real-time. By applying the extracted features on two use cases, I have shown that an adequate segmentation based on these features is possible. Furthermore, the presented process for feature extraction and selection can easily be adapted to new features. The streams framework hereby helps to implement new processors, which are capable to extract features in a real-time stream setting. Examples for more features or tags, which at the end are features as well, are mentioned above and described in the second chapter of this thesis. 90

Bibliography [itu, 2002] (2002). ITU-R BT.709: Parameter values for the HDTV standards for production and international programme exchange. International Telecommunication Union. [Aggarwal et al., 2003] Aggarwal, C. C., Han, J., Wang, J., and Yu, P. S. (2003). A framework for clustering evolving data streams. In Proceedings of the 29th International Conference on Very Large Data Bases (VLDB ’03), volume 29 of VLDB ’03, pages 81–92. VLDB Endowment. [Ahanger and Little, 1996] Ahanger, G. and Little, T. D. C. (1996). A survey of technologies for parsing and indexing digital video. Journal of visual Communication and image representation, 7:28–43. [Aigrain and Joly, 1994] Aigrain, P. and Joly, P. (1994). The automatic real-time analysis of film editing and transition effects and its applications. Computers and Graphics, 18(1):93 – 103. [Aigrain et al., 1997] Aigrain, P., Joly, P., and Longueville, V. (1997). Medium knowledge-based macro-segmentation of video into sequences. In Maybury, M. T., editor, Intelligent multimedia information retrieval, chapter Medium knowledge-based macro-segmentation of video into sequences, pages 159–173. MIT Press, Cambridge, MA, USA. [Alvarez et al., 1992] Alvarez, L., Lions, P.-L., and Morel, J.-M. (1992). Image selective smoothing and edge detection by nonlinear diffusion. ii. SIAM Journal on Numerical Analysis, 29(3):845–866. [Arasu and Manku, 2004] Arasu, A. and Manku, G. S. (2004). Approximate counts and quantiles over sliding windows. In Proceedings of the 23rd ACM SIGMOD-SIGACT- SIGART Symposium on Principles of database systems, PODS ’04, pages 286–296, New York, NY, USA. ACM. [Arman et al., 1993] Arman, F., Hsu, A., and yee Chiu, M. (1993). Feature management for large video databases. In Proceedings of SPIE Storage and Retrieval for Image and Video Databases, pages 2–12. [Avrithis et al., 2000] Avrithis, Y., Tsapatsoulis, N., and Kollias, S. (2000). Broadcast news parsing using visual cues: A robust face detection approach. In Proceedings of 91