2009 |
G Evangelopoulos, A Zlatintsi, G Skoumas, K Rapantzikos, A Potamianos, P Maragos, Y Avrithis Video Event Detection and Summarization Using Audio, Visual and Text Saliency Conference Taipei, Taiwan, 2009. Abstract | BibTeX | Links: [PDF] @conference{EZS+09, title = {Video Event Detection and Summarization Using Audio, Visual and Text Saliency}, author = {G Evangelopoulos and A Zlatintsi and G Skoumas and K Rapantzikos and A Potamianos and P Maragos and Y Avrithis}, url = {http://robotics.ntua.gr/wp-content/publications/EvangelopoulosZlatintsiEtAl_VideoEventDetectionSummarizationUsingAVTSaliency_ICASSP09.pdf}, year = {2009}, date = {2009-04-01}, address = {Taipei, Taiwan}, abstract = {Detection of perceptually important video events is formulated here on the basis of saliency models for the audio, visual and textual information conveyed in a video stream. Audio saliency is assessed by cues that quantify multifrequency waveform modulations, extracted through nonlinear operators and energy tracking. Visual saliency is measured through a spatiotemporal attention model driven by intensity, color and motion. Text saliency is extracted from part-of-speech tagging on the subtitles information available with most movie distributions. The various modality curves are integrated in a single attention curve, where the presence of an event may be signised in one or multiple domains. This multimodal saliency curve is the basis of a bottom-up video summarization algorithm, that refines results from unimodal or audiovisual-based skimming. The algorithm performs favorably for video summarization in terms of informativeness and enjoyability.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Detection of perceptually important video events is formulated here on the basis of saliency models for the audio, visual and textual information conveyed in a video stream. Audio saliency is assessed by cues that quantify multifrequency waveform modulations, extracted through nonlinear operators and energy tracking. Visual saliency is measured through a spatiotemporal attention model driven by intensity, color and motion. Text saliency is extracted from part-of-speech tagging on the subtitles information available with most movie distributions. The various modality curves are integrated in a single attention curve, where the presence of an event may be signised in one or multiple domains. This multimodal saliency curve is the basis of a bottom-up video summarization algorithm, that refines results from unimodal or audiovisual-based skimming. The algorithm performs favorably for video summarization in terms of informativeness and enjoyability. |
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