Movie keyframe retrieval based on cross-media correlation detection and context model
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
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In this paper, a novel audio-visual scene change detection algorithm is presented and evaluated experimentally. An enhanced set of eigen-audioframes is created that is related to an audio signal subspace, where audio background changes are easily discovered. An analysis is presented that justifies why this subspace favors scene change detection. Additionally, a novel process is developed in order to detect audio scene change candidates in this subspace. Visual information is used to align audio scene change indications with neighboring video shot changes and, accordingly, to reduce the false alarm rate of the audio-only scene change detection. Moreover, video fade effects are identified and used independently in order to track scene changes. The false alarm rate is reduced further by extracting acoustic features in order to verify that the scene change indications are valid. The detection methodology was tested on newscast videos provided by the TRECVID2003 video test set. The experimental results demonstrate that the proposed method achieves an F-measure exceeding 0.85. Accordingly, it effectively tackles the scene change detection problem