In this paper, a music segmentation framework is proposed to segment music streams based on human perception. In the proposed framework, three perceptual features corresponding to four perceptual properties are extracted. By analyzing the trajectory of feature values, the cutting points of a music stream can be identified. According to the complementary characteristics of the three features, a ranking algorithm is designed to achieve a better accuracy. We perform a series of experiments to evaluate the Complementary Characteristics and the effectiveness of the proposed framework.
In this paper, a video data model is proposed to represent the content of video data. In the proposed model, the trajectory and other properties of objects are recorded. From the trajectory, the motion event such as 'high speed' of an object and 'increasing distance' between objects can be automatically derived. A query language named V-SQL based on the video data model is also proposed for the users to describe the content of the desired video clips. A graphical user interface is implemented for an easier query specification.
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