Analysing the performance of visual, concept and text features in content-based video retrieval

@inproceedings{Rautiainen2004AnalysingTP,
  title={Analysing the performance of visual, concept and text features in content-based video retrieval},
  author={Mika Rautiainen and Timo Ojala and Tapio Sepp{\"a}nen},
  booktitle={Multimedia Information Retrieval},
  year={2004}
}
This paper describes revised content-based search experiments in the context of TRECVID 2003 benchmark. Experiments focus on measuring content-based video retrieval performance with following search cues: visual features, semantic concepts and text. The fusion of features uses weights and similarity ranks. Visual similarity is computed using Temporal Gradient Correlogram and Temporal Color Correlogram features that are extracted from the dynamic content of a video shot. Automatic speech… CONTINUE READING
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