Name
Abstract | ||
|---|---|---|
In this paper we present a movie summarization system and we investigate what composes high quality movie summaries in terms of user experience evaluation. We propose state-of-the-art audio, visual and text techniques for the detection of perceptually salient events from movies. The evaluation of such computational models is usually based on the comparison of the similarity between the system-detected events and some ground-truth data. For this reason, we have developed the MovSum movie database, which includes sensory and semantic saliency annotation as well as cross-media relations, for objective evaluations. The automatically produced movie summaries were qualitatively evaluated, in an extensive human evaluation, in terms of informativeness and enjoyability accomplishing very high ratings up to 80% and 90%, respectively, which verifies the appropriateness of the proposed methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/QoMEX.2015.7148146 | Quality of Multimedia Experience |
Keywords | Field | DocType |
multimedia databases,quality management,video signal processing,MovSum movie database,computational models,movie summarization system,perceptually salient events,quality evaluation | Computer science,Salience (neuroscience),Machine translation,Artificial intelligence,Language technology,User experience evaluation,Computer vision,Automatic summarization,Information retrieval,Computational linguistics,Computational model,Information extraction,Multimedia | Conference |
ISSN | Citations | PageRank |
2372-7179 | 1 | 0.35 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Zlatintsi | 1 | 78 | 2.46 |
| Katerina Pastra | 2 | 67 | 11.45 |
| Efthymiou, N. | 3 | 7 | 3.18 |
| Petros Maragos | 4 | 3733 | 591.97 |