Detail publikace

Supervised Video Scene Segmentation using Similarity Measures Supervised Video Scene Segmentation using Similarity Measures

Originální název

Supervised Video Scene Segmentation using Similarity Measures Supervised Video Scene Segmentation using Similarity Measures

Anglický název

Supervised Video Scene Segmentation using Similarity Measures Supervised Video Scene Segmentation using Similarity Measures

Jazyk

en

Originální abstrakt

Video scene segmentation is a process for dividing video into semantically meaningful blocks. This can help e.g. search engines to divide video into better manageable parts and enable more relevant search in video. Unfortunately, scene segmentation is based on the semantic and therefore it is a difficult task for computers. This work is preliminary study involved into supervised video scene segmentation, which is driven by the way how human segments scenes in a movie. Since these video segments represent semantic parts in video, it can be used for better video annotation and also for searching in videos. As a training set, only high quality movies were used and from these movies 100 training samples have been extracted and used for evaluation. Resulting model is a method based on general color layout, Tamura similarity measure and k-nearest neighbors achieving 97.00% accuracy.

Anglický abstrakt

Video scene segmentation is a process for dividing video into semantically meaningful blocks. This can help e.g. search engines to divide video into better manageable parts and enable more relevant search in video. Unfortunately, scene segmentation is based on the semantic and therefore it is a difficult task for computers. This work is preliminary study involved into supervised video scene segmentation, which is driven by the way how human segments scenes in a movie. Since these video segments represent semantic parts in video, it can be used for better video annotation and also for searching in videos. As a training set, only high quality movies were used and from these movies 100 training samples have been extracted and used for evaluation. Resulting model is a method based on general color layout, Tamura similarity measure and k-nearest neighbors achieving 97.00% accuracy.

BibTex


@inproceedings{BUT100831,
  author="Radim {Burget} and Jan {Mašek} and Malay Kishore {Dutta} and Václav {Uher}",
  title="Supervised Video Scene Segmentation using Similarity Measures Supervised Video Scene Segmentation using Similarity Measures",
  annote="Video scene segmentation is a process for dividing video into semantically meaningful blocks. This can help e.g. search engines to divide video into better manageable parts and enable more relevant search in video. Unfortunately, scene segmentation is based on the semantic and therefore it is a difficult task for computers.  This work is preliminary study involved into supervised video scene segmentation, which is driven by the way how human segments scenes in a movie. Since these video segments represent semantic parts in video, it can be used for better video annotation and also for searching in videos. As a training set, only high quality movies were used and from these movies 100 training samples have been extracted and used for evaluation. Resulting model is a method based on general color layout, Tamura similarity measure and k-nearest neighbors achieving 97.00% accuracy.",
  booktitle="36th International Conference on Telecommunications and Signal processing",
  chapter="100831",
  howpublished="print",
  year="2013",
  month="july",
  pages="793--797",
  type="conference paper"
}