Abstract
With the rapid growth of video multimedia databases and the lack of textual descriptions for many of them, video annotation became a highly desired task. Conventional systems try to annotate a video query by simply finding its most similar videos in the database. Although the video annotation problem has been tackled in the last decade, no attention has been paid to the problem of assembling video keyframes in a sensed way to provide an answer of the given video query when no single candidate video turns out to be similar to the query. In this paper, we introduce a graph based image modeling and indexing system for video annotation. Our system is able to improve the video annotation task by assembling a set of graphs representing different keyframes of different videos, to compose the video query. The experimental results demonstrate the effectiveness of our system to annotate videos that are not possibly annotated by classical approaches.
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
Pramod Sankar, K., Meshesha, M., Jawahar, C.V.: Annotation of Images and videos based on Textual Content without OCR. In: Workshop on Computation Intensive Methods for Computer Vision (in conjunction with ECCV 2006) (2006)
Ben Aoun, N., Elghazel, H., Ben Amar, C.: Graph modeling based video event detection. In: International Conference on Innovations in Information Technology, Abu Dhabi, United Arab Emirates (2011)
Sukhwinder Bir, S., Kaur, A.: Color Image Segmentation in CIEab Space Using Hill Climbing Algorithm. The International Journal of Computer Applications 7(3), 48–53 (2010)
Petrakis, E., Faloutsos, C.: Similarity Searching in Medical Image Databases. IEEE Transactions on Knowledge and Data Engineering 9(3), 435–447 (1997)
Iváncsy, G., Iváncsy, R., Vajk, I.: Graph Mining-based Image Indexing. In: 5th International Symposium of Hungarian Researchers on Computational Intelligence, Budapest, Hungary, pp. 313–323 (2004)
Elsayed, A., Coenen, F., Jiang, C., GarcÃa-Finana, M., Sluming, V.: Corpus Callosum MR image classification. Knowledge-Based Systems 23, 330–336 (2010)
Yan, X., Han, J.: gSpan: graph-based substructure pattern mining. In: The Proceeding of the IEEE International Conference on Data Mining (ICDM 2002), Maebashi City, Japan, pp. 721–724 (2002)
Shang, H., Zhang, Y., Lin, X., Yu, J.X.: Taming verification hardness: an efficient algorithm for testing subgraph isomorphism. In: International Conference on Very Large Data Bases, pp. 364–375 (2008)
Elghazel, H., Hacid, M.: Aggregated Search in Graph Databases: Preliminary Results. In: 8th IAPR-TC-15 Workshop on Graph-based Representations in Pattern Recognition (GbR 2011), Munster, Germany (2011)
Raymond, J.W., Willett, P.: Maximum common subgraph isomorphism algorithms for the matching of chemical structures. Journal of Computer-Aided Molecular Design 16(7), 521–533 (2002)
Wali, A., Ben Aoun, N., Karray, H., Ben Amar, C., Alimi, A.M.: A new system for event detection from video surveillance sequences. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2010, Part II. LNCS, vol. 6475, pp. 110–120. Springer, Heidelberg (2010)
Ben Aoun, N., El’Arbi, M. Ben Amar, C.: Multiresolution motion estimation and compensation for video coding. In: The 10th IEEE International Conference on Signal Processing (ICSP 2010), Beijing, China, pp. 1121–1124 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ben Aoun, N., Elghazel, H., Hacid, MS., Ben Amar, C. (2011). Graph Aggregation Based Image Modeling and Indexing for Video Annotation. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23678-5_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-23678-5_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23677-8
Online ISBN: 978-3-642-23678-5
eBook Packages: Computer ScienceComputer Science (R0)