semantic concept annotation based on audio plsa model
This paper proposes a new approach and algorithm for the semantic concept annotation based on audio PLSA (probabilistic latent semantic analysis) model. The novelty of our approach includes two sides: Audio vocabulary construction, and audio PLSA model. In audio vocabulary construction, we first segment an audio-clip into a few homogeneous audio-segments according to its content change, which not only capture the change property of audio-clip, but also keep and present the change relation and temporal order of audio features. Then an audio vocabulary is constructed by the RPCL (rival penalized competitive learning) clustering of audio-segments. In this way, each audio-clip can be represented by a bag-of-word form. In audio PLSA model, PLSA is employed to discover the latent topics existing in audio-clips. Based on the discovered topics, the concept classification is then carried out by a support vector machine (SVM) classifier. In addition, we also combine the local features extracted by PLSA and global features in audio-clip to further improve the performance of concept annotation. The experiments are evaluated on 85 hours of audio data from the TRECVID 2005, and show the encouraging results of our approach.
concept annotation audio plsa model audio vocabulary