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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Semantic Concept Annotation of Consumer Videos at Frame-Level Using Audio
verfasst von : Junwei Liang, Qin Jin, Xixi He, Gang Yang, Jieping Xu, Xirong Li
Erschienen in: Advances in Multimedia Information Processing – PCM 2014
Verlag: Springer International Publishing
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With the increasing use of audio sensors in user generated content (UGC) collection, semantic concept annotation using audio streams has become an important research problem. Huawei initiates a grand challenge in the International Conference on Multimedia & Expo (ICME) 2014: Huawei Accurate and Fast Mobile Video Annotation Challenge. In this paper, we present our semantic concept annotation system using audio stream only for the Huawei challenge. The system extracts audio stream from the video data and low-level acoustic features from the audio stream. Bag-of-feature representation is generated based on the low-level features and is used as input feature to train the support vector machine (SVM) concept classifier. The experimental results show that our audio-only concept annotation system can detect semantic concepts significantly better than random guess. It can also provide important complementary information to the visual-based concept annotation system for performance boost.