A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

  • Authors:

  • Sébastien Lefèvre;Jérôme Holler;Nicole Vincent

  • Affiliations:

  • Laboratoire d'Informatique, E31, Université de Tours, 64, Avenue Portalis, 37200 Tours, France and AtosOrigin, 19, Rue de la Vallée Maillard, BP 1311, 41013 Blois Cedex, France;Laboratoire d'Informatique, E31, Université de Tours, 64, Avenue Portalis, 37200 Tours, France;Laboratoire d'Informatique, E31, Université de Tours, 64, Avenue Portalis, 37200 Tours, France

  • Venue:
  • Real-Time Imaging
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present in this paper a review of methods for segmentation of uncompressed video sequences. Video segmentation is usually performed in the temporal domain by shot change detection. In case of real-time segmentation, computational complexity is one of the criteria which has to be taken into account when comparing different methods. When dealing with uncompressed video sequences, this criterion is even more significant. However, previous published reviews did not involve complexity criterion when comparing shot change detection methods. Only recognition rate and ability to classify detected shot changes were considered. So contrary to previous reviews, we give here the complexity of most of the described methods. We review in this paper an extensive set of methods presented in the literature and classify them in several parts, depending on the information used to detect shot changes. The earliest methods were comparing successive frames by relying on the most simple elements, that is to say pixels. Comparison could be performed on a global level, so methods based on histograms were also proposed. Block-based methods have been considered to process data at an intermediate level, between local (using pixels) and global (using histograms) levels. More complex features can be involved, resulting in feature-based methods. Alternatively some methods rely on motion as a criterion to detect shot changes. Finally, different kinds of information could be combined together in order to increase the quality of shot change detection. So our review will detail segmentation methods based on the following information: pixel, histogram, block, feature, motion, or other kind of information.