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Content-Based Copy Detection (CBCD) of digital videos is an important research field that aims at the identification of modified copies of an original clip, e.g., on the Internet. In this application, the video content is uniquely identified by the content itself, by extracting some compact features that are robust to a certain set of video transformations. Given the huge amount of data present in...
This paper focuses on a specific type of unedited video content, called rushes, which are used for movie editing and usually present a high-level of redundancy. Our goal is to automatically extract a summarized preview, where redundant material is diminished without discarding any important event. To achieve this, rushes content has been first analysed and modeled. Then different clustering techniques...
In this paper we present a statistical framework based on hidden Markov models (HMMs) for video skimming. A chain of HMMs is used to model subsequent story units: HMM states represent different visual-concepts, transitions model the temporal dependencies in each story unit, and stochastic observations are given by single shots. The skim is generated as an observation sequence, where, in order to privilege...
In this work we present a method for video skimming based on hidden Markov Models (HMMs) and motion activity. Specifically, a set of HMMs is used to model subsequent logical story units, where the HMM states represent different visual-concepts, the transitions model the temporal dependencies in each story unit, and stochastic observations are given by single shots. The video skim is generated as an...
In this paper we consider the problem of image encoding with side information at the decoder, where the side information is an integer shifted version of the image at the encoder. The encoder is asked to send the shift of its own image with respect to the side information which is only available at the decoder. We propose a solution based on the encoding of the phase sign of the DFT coefficients,...
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