COGNIMUSE: a multimodal video database annotated with saliency, events, semantics and emotion with application to summarization

COGNIMUSE: a multimodal video database annotated with saliency, events, semantics and emotion... Research related to computational modeling for machine-based understanding requires ground truth data for training, content analysis, and evaluation. In this paper, we present a multimodal video database, namely COGNIMUSE, annotated with sensory and semantic saliency, events, cross-media semantics, and emotion. The purpose of this database is manifold; it can be used for training and evaluation of event detection and summarization algorithms, for classification and recognition of audio-visual and cross-media events, as well as for emotion tracking. In order to enable comparisons with other computational models, we propose state-of-the-art algorithms, specifically a unified energy-based audio-visual framework and a method for text saliency computation, for the detection of perceptually salient events from videos. Additionally, a movie summarization system for the automatic production of summaries is presented. Two kinds of evaluation were performed, an objective based on the saliency annotation of the database and an extensive qualitative human evaluation of the automatically produced summaries, where we investigated what composes high-quality movie summaries, where both methods verified the appropriateness of the proposed methods. The annotation of the database and the code for the summarization system can be found at http://cognimuse.cs.ntua.gr/database . http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png EURASIP Journal on Image and Video Processing Springer Journals

COGNIMUSE: a multimodal video database annotated with saliency, events, semantics and emotion with application to summarization

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Publisher
Springer International Publishing
Copyright
Copyright © 2017 by The Author(s)
Subject
Engineering; Signal,Image and Speech Processing; Image Processing and Computer Vision; Biometrics; Pattern Recognition
eISSN
1687-5281
D.O.I.
10.1186/s13640-017-0194-1
Publisher site
See Article on Publisher Site

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