Abstract
In this paper we present an interactive, object-based video retrieval system which features a novel query formulation method that is used to iteratively refine an underlying model of the search object. As the user continues query composition and browsing of retrieval results, the system’s object modeling process, based on Gaussian probability distributions, becomes incrementally more accurate, leading to better search results. To make the interactive process understandable and easy to use, a custom user-interface has been designed and implemented that allows the user to interact with segmented objects in formulating a query, in browsing a search result, and in re-formulating a query by selecting an object in the search result.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Smeulders, A.W., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-Based Image Retrieval at the End of the Early Years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1349–1380 (2000)
Smith, J.R., Chang, S.F.: VisualSEEK: a fully automated content-based image query system. ACM Multimedia, Boston (November 1996)
Carson, C., Thomas, M., Belongie, S., Hellerstein, J.M., Malik, J.: A System for Region-Based Image Indexing and Retrieval. In: Proceedings of the Third International Conference on Visual Information and Information Systems (1999)
Rui, Y., Huang, T.S., Mehrotra, S.: Content-based image retrieval with relevance feed-back in Mars. In: Proceedings of IEEE International Conference on Image Processing ICIP (1997)
Yan, R., Hauptmann, A., Jin, R.: Multimedia Search with Pseudo -Relevance Feedback. In: Proceedings of International Conference on Image and Video Retrieval CIVR 2003, Urbana, IL, July 24-25 (2003)
O’Connor, N., Adamek, T., Sav, S., Murphy, N., Marlow, S.: QIMERA: A Software Platform for Video Object Segmentation and Tracking. In: Proceedings of the 4th Workshop on Image Analysis for Multimedia Interactive Service (WIAMIS 2003), London, U.K., April 9-11 (2003)
Salambier, P., Smith, J.R.: MPEG-7 Multimedia Descriptions Schemes. IEEE Transactions on Circuits and Systems for Video Technology 11, 748–759 (2001)
Qian, F., Li, M., Zhang, L., Zhang, H.J., Zhang, B.: Gaussian mixture model for relevance feedback in image retrieval. In: Proceeding of IEEE International Conference on Multimedia and Expo, Lausanne, Switzerland (August 2002)
Moon, T.K.: The Expectation-Maximisation Algorithm. In: IEEE Signal Processing Magazine (November 1996)
Fessant, F., Aknin, P., Oukhellou, L., Midenet, S.: Comparison of supervised self-organizing maps using Euclidian or Mahalanobis distance in classification context. In: Mira, J., Prieto, A.G. (eds.) IWANN 2001. LNCS, vol. 2084, p. 637. Springer, Heidelberg (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sav, S., Lee, H., O’Connor, N., Smeaton, A.F. (2005). Interactive Object-Based Retrieval Using Relevance Feedback. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_33
Download citation
DOI: https://doi.org/10.1007/11558484_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29032-2
Online ISBN: 978-3-540-32046-3
eBook Packages: Computer ScienceComputer Science (R0)