Abstract
Recent advances in digital video technology have resulted in an explosion of digital video data which are available through the Web or in private repositories. Efficient searching in these repositories created the need of semantic labeling of video data at various levels of granularity, i.e., movie, scene, shot, keyframe, video object, etc. Through multilevel labeling video content is appropriately indexed, allowing access from various modalities and for a variety of applications. However, despite the huge efforts for automatic video annotation human intervention is the only way for reliable semantic video annotation. Manual video annotation is an extremely laborious process and efficient tools developed for this purpose can make, in many cases, the true difference. In this paper we present a video annotation tool, which uses structured knowledge, in the form of XML dictionaries, combined with a hierarchical classification scheme to attach semantic labels to video segments at various level of granularity. Video segmentation is supported through the use of an efficient shot detection algorithm; while shots are combined into scenes through clustering with the aid of a Genetic Algorithm scheme. Finally, XML dictionary creation and editing tools are available during annotation allowing the user to always use the semantic label she/he wishes instead of the automatically created ones.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lin, C.Y., Tseng, L., Smith, R.: Video Collaboration Annotation Forum: Establishing Ground-Truth Labels on Large Multimedia Datasets. In: Proc. of NIST Text Retrieval Conference (TREC) (November 2003)
Ricoh Movie Tool website, http://www.ricoh.co.jp/src/multimedia/MovieTool
Adams, W.H., Lin, C.Y., Iyengar, B., Tseng, B.L., Smith, J.R.: IBM Multimedia Annotation Tool. IBM Alphaworks (August 2002)
Bargeron, D., Gupta, A., Grudin, J., Sanocki, E.: Annotations for Streaming Video on the Web:System Design and usage Studies. In: Proc. ACM 8th Conference on World Wide Web, Torondo, Canada (1999)
European Cultural Heritage Online (ECHO), http://www.mpi.nl/echo/
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
ISO/IEC 15938-3:2001 Information Technology - Multimedia Content Description Interface - Part 3: Visual, Version 1
ISO/IEC 15938-4:2001 Information Technology - Multimedia Content Description Interface - Part 4: Audio, Version 1
ISO/IEC 15938-5:2003 Information Technology - Multimedia Content Description Interface - Part 5: Multimedia Description Schemes, First edn.
Lienhart, R.: Comparison of Automatic Shot Boundary Detection Algorithms. In: Proc. of SPIE, Storage and Retrieval for Image and Video Databases VII, San Jose, CA, USA, vol. 3656, pp. 290–301 (1999)
Nack, F., Putz, W.: Semi-automated Annotation of Audio-Visual Media in News. GMD Report 121 (2000)
Steves, M.P., Ranganathan, M., Morse, E.L.: SMAT:Synchronous Multimedia and Annotation Tool. In: Proc. of 34th Hawaii International Conference on Systems Sciences (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Theodosiou, Z., Kounoudes, A., Tsapatsoulis, N., Milis, M. (2009). MuLVAT: A Video Annotation Tool Based on XML-Dictionaries and Shot Clustering. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_92
Download citation
DOI: https://doi.org/10.1007/978-3-642-04277-5_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
Online ISBN: 978-3-642-04277-5
eBook Packages: Computer ScienceComputer Science (R0)