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Effective similarity search in multi-media time series such as video or audio sequences is important for content-based multi-media retrieval applications. The authors propose a framework that extracts a sequence of local features from large multi-media time series that reflect the characteristics of the complex structured time series more accurately than global features. In addition, they propose a set of suitable local features that can be derived by the framework. These features are scanned from a time series amplitude-levelwise and are called amplitude-level features. Their experimental evaluation shows that their method models the intuitive similarity of multi-media time series better than existing techniques.
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