A Relevant Visual Feature Selection Approach for Image Retrieval

Olfa Allani, Nedra Mellouli, Hajer Baazaoui Zghal, Herman Akdag, Henda Ben Ghzala

2015

Abstract

Content-Based Image Retrieval approaches have been marked by the semantic gap (inconsistency) between the perception of the user and the visual description of the image. This inconsistency is often linked to the use of predefined visual features randomly selected and applied whatever the application domain. In this paper we propose an approach that adapts the selection of visual features to semantic content ensuring the coherence between them. We first design visual and semantic descriptive ontologies. These ontologies are then explored by association rules aiming to link semantic descriptor (a concept) to a set of visual features. The obtained feature collections are selected according to the annotated query images. Different strategies have been experimented and their results have shown an improvement of the retrieval task based on relevant feature selections.

References

  1. Akdag, H., Mellouli, N., and Borgi, A. (2000). A symbolic approach of linguistic modifiers. In Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU).
  2. Allani, O., Baazaoui-Zghal, H., Mellouli, N., Ben-Ghezala, H., and Akdag, H. (2014). A pattern-based system for image retrieval. In International Conference on Knowledge Engineering and Ontology Development.
  3. Besbes, G. and Baazaoui Zghal, H. (2014). Modular ontologies and cbr-based hybrid system for web information retrieval. Journal of Multimedia Tools and Applications.
  4. Deselaers, T., Keysers, D., and Ney, H. (2008). Features for image retrieval: An experimental comparison. Information Retrieval, 11.
  5. Hafiane, A. and Zavidovique, B. (2008). Local relational string and mutual matching for image retrieval. Information Processing & Management, 44.
  6. Hejazi, M. R. and Ho, Y.-S. (2007). An efficient approach to texture-based image retrieval. International Journal of Imaging Systems and Technology, 17.
  7. Hiremath, P. S. and Pujari, J. (2007). P. s. hiremath and jagadeesh pujari content based image retrieval based on color, texture and shape features using image and its complement.
  8. Jalab, H. A. (2011). Image retrieval system based on color layout descriptor and gabor filters. In Open Systems (ICOS), 2011 IEEE Conference on. IEEE.
  9. Lavenier, D. (2001). Paralllisation de lalgorithme du kmeans sur un systme reconfigurable application aux images hyper-spectrales. In Traitement du Signal Volume 18.
  10. Lin, H.-J., Kao, Y.-T., Yen, S.-H., and Wang, C.-J. (2004). A study of shape-based image retrieval. In Distributed Computing Systems Workshops, 2004. Proceedings. 24th International Conference on. IEEE.
  11. Liu, Y., Zhang, D., Lu, G., and Ma, W.-Y. (2007). A survey of content-based image retrieval with high-level semantics. Pattern Recogn., 40.
  12. Mussarat, Y., Sharif, M., and Mohsin, S. (2013). Use of low level features for content based image retrieval: Survey. Research Journal of Recent Sciences, 2277.
  13. Paschos, G., Radev, I., and Prabakar, N. (2003). Image content-based retrieval using chromaticity moments. Knowledge and Data Engineering, IEEE Transactions on, 15.
  14. Pass, G., Zabih, R., and Miller, J. (1996). Comparing images using color coherence vectors. In Aigrain, P., Hall, W., Little, T. D. C., and Jr., V. M. B., editors, ACM Multimedia.
  15. Sarfraz, M. S. and Hellwich, O. (2008). Head pose estimation in face recognition across pose scenarios. In International Conference on Computer Vision Theory and Applications. INSTICC - Institute for Systems and Technologies of Information, Control and Communication.
  16. Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., and Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell., 22.
  17. Vinukonda, P. (2011). A Study of the Scale-Invariant Feature Transform on a Parallel Pipeline. PhD thesis, Louisiana State University.
  18. Viola, P. and Jones, M. (2004). Robust real-time face detection. International Journal of Computer Vision, 57.
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Paper Citation


in Harvard Style

Allani O., Mellouli N., Baazaoui Zghal H., Akdag H. and Ghzala H. (2015). A Relevant Visual Feature Selection Approach for Image Retrieval . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 377-384. DOI: 10.5220/0005306303770384


in Bibtex Style

@conference{visapp15,
author={Olfa Allani and Nedra Mellouli and Hajer Baazaoui Zghal and Herman Akdag and Henda Ben Ghzala},
title={A Relevant Visual Feature Selection Approach for Image Retrieval},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={377-384},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005306303770384},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - A Relevant Visual Feature Selection Approach for Image Retrieval
SN - 978-989-758-090-1
AU - Allani O.
AU - Mellouli N.
AU - Baazaoui Zghal H.
AU - Akdag H.
AU - Ghzala H.
PY - 2015
SP - 377
EP - 384
DO - 10.5220/0005306303770384