HETEROGENEOUS IMAGE RETRIEVAL SYSTEM BASED ON FEATURES EXTRACTION AND SVM CLASSIFIER

Rostom Kachouri, Khalifa Djemal, Hichem Maaref, Dorra Sellami Masmoudi, Nabil Derbel

2008

Abstract

Image databases represent increasingly important volume of information, so it is judicious to develop powerful systems to handle the images, index them, classify them to reach them quickly in these large image databases. In this paper, we propose an heterogeneous image retrieval system based on feature extraction and Support vector machines (SVM) classifier. For an heterogeneous image database, first of all we extract several feature kinds such as color descriptor, shape descriptor, and texture descriptor. Afterwards we improve the description of these features, by some original methods. Finally we apply an SVM classifier to classify the consequent index database. For evaluation purposes, using precision/recall curves on an heterogeneous image database, we looked for a comparison of the proposed image retrieval system with an other Content-based image retrieval (CBIR) which is QUadtree-based Index for image retrieval and Pattern search (QUIP-tree). The obtained results show that the proposed system provides good accuracy recognition, and it prove more better than QUIP-tree method.

References

  1. Bimbo, A. (2001). Visual information retrieval. Morgan Kaufmann Publishers.
  2. Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discovery, 2(2):121-167.
  3. Genevire, J., Maude, M., Vincent, O., and Marta, R. (2004). Indexation multi-niveau pour la recherche globale et partielle d'images par le contenu. In BDA.
  4. Hu, M. (1962). Visual pattern recognition by moment invariants. IEEE Transactions information Theory, 8:179-187.
  5. Julesz, B., Gilbert, E., and Victor, J. (1978). Visual discrimination of textures with identical third-order statistics. Biol. Cybern., 31:137-140.
  6. Kachouri, R., Djemal, K., Sellami-Masmoudi, D., Maaref, H., and Derbel, N. (2007). On the heterogeneous image retrieval with quip-tree. In SSD.
  7. Manouvrier, M., Rukoz, M., and Jomier, G. (2005). Spatial Databases : Technologies, Techniques and Trend, Quadtree-Based Image Representation and Retrieval, chapter 4, pages 81-106. IDEA Group Publishing, Information Science Publishing and IRM Press.
  8. Osuna, E., Freund, R., and Girosi, F. (1997). Training support vector machines: an application to face detection.
  9. Schokopf, B., Burges, C., and Smola, A. (1999). Introduction to support vector learning, chapter 1. Advances in Kernel Methods - Support Vector Learning.
  10. Serrano, N., Savakisb, A., and Luoc, J. (2004). Improved scene classification using fficient low-level features and semantic cues. Pattern Recognition, 37:1773- 1784.
  11. Smith, J. and Chang, S. (1996). Tools and techniques for colour image retrieval. In IS T/SPIE Proceedings, volume 2670, pages 426-437, San Jose, CA, USA.
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Paper Citation


in Harvard Style

Kachouri R., Djemal K., Maaref H., Sellami Masmoudi D. and Derbel N. (2008). HETEROGENEOUS IMAGE RETRIEVAL SYSTEM BASED ON FEATURES EXTRACTION AND SVM CLASSIFIER . In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO, ISBN 978-989-8111-32-6, pages 137-142. DOI: 10.5220/0001490601370142


in Bibtex Style

@conference{icinco08,
author={Rostom Kachouri and Khalifa Djemal and Hichem Maaref and Dorra Sellami Masmoudi and Nabil Derbel},
title={HETEROGENEOUS IMAGE RETRIEVAL SYSTEM BASED ON FEATURES EXTRACTION AND SVM CLASSIFIER},
booktitle={Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,},
year={2008},
pages={137-142},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001490601370142},
isbn={978-989-8111-32-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,
TI - HETEROGENEOUS IMAGE RETRIEVAL SYSTEM BASED ON FEATURES EXTRACTION AND SVM CLASSIFIER
SN - 978-989-8111-32-6
AU - Kachouri R.
AU - Djemal K.
AU - Maaref H.
AU - Sellami Masmoudi D.
AU - Derbel N.
PY - 2008
SP - 137
EP - 142
DO - 10.5220/0001490601370142