Real-time feature extraction from video stream data for stream ...

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Real-time feature extraction from video stream data for stream ...

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.

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