Abstract
In order to provide more efficient content-based functionalities for video applications, it is necessary to extract meaningful video regions from scenes as perceptual-oriented representation of video content. We present a generalpurpose content representation framework for video sequences that employs fuzzy information granulation to capture human perception subjectivity. In particular, the main purpose is to extract spatial-temporal salient grain that is fundamental element for content representation of video sequences. Since perceptual saliency for visual information is a subjective concept, a class-related fuzzy information granulation is constructed for each feature of homogenous regions, mapping original feature space to concepts space. To detect spatial salient regions, segmented homogenous regions are classified according to their prominent importance. After salient region detection, a region tracking mechanism is proposed based on region temporal consistency analysis. The tracking results are sequences of coherent salient regions, called spatial-temporal salient grain. Salient grain can be used to obtain meaningful perceptual-oriented unit in a high-level content description scheme. The experimental results using different types of video sequences are presented to demonstrate the efficiency and accuracy of our proposed algorithm.
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Lang, C., Xu, D., Yu, J. (2007). The Use of Fuzzy Information Granular for Content Representation of Video Sequences. In: Nachtegael, M., Van der Weken, D., Kerre, E.E., Philips, W. (eds) Soft Computing in Image Processing. Studies in Fuzziness and Soft Computing, vol 210. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-38233-1_10
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DOI: https://doi.org/10.1007/978-3-540-38233-1_10
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