OPTIMAL COMBINATION OF LOW-LEVEL FEATURES FOR SURVEILLANCE OBJECT RETRIEVAL

Virginia Fernandez Arguedas, Krishna Chandramouli, Qianni Zhang, Ebroul Izquierdo

2011

Abstract

In this paper, a low-level multi-feature fusion based classifier is presented for studying the performance of an object retrieval method from surveillance videos. The proposed retrieval framework exploits the recent developments in evolutionary computation algorithm based on biologically inspired optimisation techniques. The multi-descriptor space is formed with a combination of four MPEG-7 visual features. The proposed approach has been evaluated against kernel machines for objects extracted from AVSS 2007 dataset.

References

  1. Chandramouli, K. and Izquierdo, E. (2010). Image Retrieval using Particle Swarm Optimization, pages 297-320. CRC Press.
  2. Chandramouli, K. and Izquierdo, E. (2010). Image Retrieval using Particle Swarm Optimization, pages 297-320. CRC Press.
  3. Eberhart, R. and Shi, Y. (2001). Tracking and optimizing dynamic systems with particle swarms. Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, 1.
  4. Eberhart, R. and Shi, Y. (2001). Tracking and optimizing dynamic systems with particle swarms. Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, 1.
  5. Kennedy, J. and Eberhart, R. C. (2001). Swarm intelligence. Morgan Kaufmann.
  6. Kennedy, J. and Eberhart, R. C. (2001). Swarm intelligence. Morgan Kaufmann.
  7. Mojsilovic, A. (2005). A computational model for color naming and describing color composition of images. Image Processing, IEEE Trans., 14(5):690-699.
  8. Mojsilovic, A. (2005). A computational model for color naming and describing color composition of images. Image Processing, IEEE Trans., 14(5):690-699.
  9. Sikora, T. (2002). The MPEG-7 Visual standard for content description-an overview. Circuits and Systems for Video Technology, IEEE Trans., 11(6):696-702.
  10. Sikora, T. (2002). The MPEG-7 Visual standard for content description-an overview. Circuits and Systems for Video Technology, IEEE Trans., 11(6):696-702.
  11. Soysal, M. and Alatan, A. (2003). Combining MPEG-7 based visual experts for reaching semantics. Visual Content Processing and Representation, pages 66-75.
  12. Soysal, M. and Alatan, A. (2003). Combining MPEG-7 based visual experts for reaching semantics. Visual Content Processing and Representation, pages 66-75.
  13. Stauffer, C. and Grimson, W. (2000). Learning patterns of activity using real-time tracking. Pattern Analysis and Machine Intelligence, IEEE Trans., 22(8):747-757.
  14. Stauffer, C. and Grimson, W. (2000). Learning patterns of activity using real-time tracking. Pattern Analysis and Machine Intelligence, IEEE Trans., 22(8):747-757.
  15. Xu, R. and II, D. W. (2005). Survey of clustering algorithms. IEEE Trans. Neural Network, 6(3):645-678.
  16. Xu, R. and II, D. W. (2005). Survey of clustering algorithms. IEEE Trans. Neural Network, 6(3):645-678.
  17. Zhang, Q. and Izquierdo, E. (2007). Combining low-level features for semantic inference in image retrieval. Journal on Advances in Signal Processing.
  18. Zhang, Q. and Izquierdo, E. (2007). Combining low-level features for semantic inference in image retrieval. Journal on Advances in Signal Processing.
Download


Paper Citation


in Harvard Style

Fernandez Arguedas V., Chandramouli K., Zhang Q. and Izquierdo E. (2011). OPTIMAL COMBINATION OF LOW-LEVEL FEATURES FOR SURVEILLANCE OBJECT RETRIEVAL . In Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2011) ISBN 978-989-8425-72-0, pages 187-192. DOI: 10.5220/0003527101870192


in Harvard Style

Fernandez Arguedas V., Chandramouli K., Zhang Q. and Izquierdo E. (2011). OPTIMAL COMBINATION OF LOW-LEVEL FEATURES FOR SURVEILLANCE OBJECT RETRIEVAL . In Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2011) ISBN 978-989-8425-72-0, pages 187-192. DOI: 10.5220/0003527101870192


in Bibtex Style

@conference{sigmap11,
author={Virginia Fernandez Arguedas and Krishna Chandramouli and Qianni Zhang and Ebroul Izquierdo},
title={OPTIMAL COMBINATION OF LOW-LEVEL FEATURES FOR SURVEILLANCE OBJECT RETRIEVAL},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2011)},
year={2011},
pages={187-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003527101870192},
isbn={978-989-8425-72-0},
}


in Bibtex Style

@conference{sigmap11,
author={Virginia Fernandez Arguedas and Krishna Chandramouli and Qianni Zhang and Ebroul Izquierdo},
title={OPTIMAL COMBINATION OF LOW-LEVEL FEATURES FOR SURVEILLANCE OBJECT RETRIEVAL},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2011)},
year={2011},
pages={187-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003527101870192},
isbn={978-989-8425-72-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2011)
TI - OPTIMAL COMBINATION OF LOW-LEVEL FEATURES FOR SURVEILLANCE OBJECT RETRIEVAL
SN - 978-989-8425-72-0
AU - Fernandez Arguedas V.
AU - Chandramouli K.
AU - Zhang Q.
AU - Izquierdo E.
PY - 2011
SP - 187
EP - 192
DO - 10.5220/0003527101870192


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2011)
TI - OPTIMAL COMBINATION OF LOW-LEVEL FEATURES FOR SURVEILLANCE OBJECT RETRIEVAL
SN - 978-989-8425-72-0
AU - Fernandez Arguedas V.
AU - Chandramouli K.
AU - Zhang Q.
AU - Izquierdo E.
PY - 2011
SP - 187
EP - 192
DO - 10.5220/0003527101870192