Classification of Semantic Concepts to Support the Analysis of the Inter-Cultural Visual Repertoires of TV News Reviews (bibtex)
by Martin Stommel, Martina Dümcke, Otthein Herzog
Abstract:
TV news reviews are of strong interest in media and communication sciences, since they indicate national and international social trends. To identify such trends, scientists from these disciplines usually work with manually annotated video data. In this paper, we investigate if the time-consuming process of manual annotation can be automated by using the current pattern recognition techniques. To this end, a comparative study on different combinations of local and global features sets with two examples of the pyramid match kernel is conducted. The performance of the classification of TV new scenes is measured. The classes are taken from a coding scheme that is the result of an international discourse in media and communication sciences. For the classification of studio vs. non-studio, football vs. ice hockey, computer graphics vs. natural scenes and crowd vs. no crowd, recognition rates between 80 and 90 percent could be achieved.
Reference:
Martin Stommel, Martina Dümcke, Otthein Herzog, "Classification of Semantic Concepts to Support the Analysis of the Inter-Cultural Visual Repertoires of TV News Reviews", In German Conference on Artificial Intelligence, Springer, Berlin, 2011.
Bibtex Entry:
@INPROCEEDINGS{Stommel2011c,
  author = {Stommel, Martin and D{\"u}mcke, Martina and Herzog, Otthein},
  title = {Classification of Semantic Concepts to Support the Analysis of the
	Inter-Cultural Visual Repertoires of TV News Reviews},
  booktitle = {German Conference on Artificial Intelligence},
  year = {2011},
  series = {Lecture Notes in Artificial Intelligence},
  address = {Berlin},
  month = {October4--7},
  publisher = {Springer},
  abstract = {TV news reviews are of strong interest in media and communication
	sciences, since they indicate national and international social trends.
	To identify such trends, scientists from these disciplines usually
	work with manually annotated video data. In this paper, we investigate
	if the time-consuming process of manual annotation can be automated
	by using the current pattern recognition techniques. To this end,
	a comparative study on different combinations of local and global
	features sets with two examples of the pyramid match kernel is conducted.
	The performance of the classification of TV new scenes is measured.
	The classes are taken from a coding scheme that is the result of
	an international discourse in media and communication sciences. For
	the classification of studio vs. non-studio, football vs. ice hockey,
	computer graphics vs. natural scenes and crowd vs. no crowd, recognition
	rates between 80 and 90 percent could be achieved.},
  owner = {pmania},
  timestamp = {2012.11.06}
}
Powered by bibtexbrowser