Publication detail

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

BURGET, R. RAY, J.,K.UHER, V. MAŠEK, J. DUTTA, M.

Original Title

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

English Title

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

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

image analysis, machine learning, similarity measure, video segmentation.

RIV year

2013

Released

02.07.2013

ISBN

978-1-4799-0402-0

Book

36th International Conference on Telecommunications and Signal processing

Pages from

793

Pages to

797

Pages count

912

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"
}