AN EVALUATION OF LOCAL IMAGE FEATURES FOR OBJECT CLASS RECOGNITION

Saiful Islam, Andrzej Sluzek

Abstract

The use of local image features (LIF) for object class recognition is becoming increasingly popular. To better understand the suitability and power of existing LIFs for object class recognition, a simple but useful method is proposed in evaluation of such features. We have compared the performance of eight frequently used LIFs by the proposed method on two popular databases. We have used F-measure criterion for this evaluation. It is found that the individual performance of SURF and SIFT features are better than that of the global features on ETH-80* database with considerably lower number of training objects. However, it may not be good enough for more challenging object class recognition problem (e.g. Caltech-101+). The evaluation of LIFs suggests the requirement for further investigation of more complementary LIFs.

References

  1. Asbach, M., Hosten, P. and Unger, M. (2008). An Evaluation of Local Features for Face Detection and Localization. Ninth Int. Workshop on Image Analysis for Multimedia Interactive Services.
  2. Bay, H., Ess, A., Tuytelaars, T. and Gool, L. V. (2008). Speeded-up Robust Features (SURF). Computer Vision and Image Understanding 110, 346-359
  3. Boiman, O., Shechtman, E. and Irani, M. (2008). In Defense of Nearest-Neighbor Based Image Classification. IEEE Conf. on CVPR, 1 - 8.
  4. Freeman, W. and Adelson, E. (1991). The Design and Use of Steerable Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence: 13, 891-906.
  5. Leibe, B. and Schiele, B. (2003). Analyzing Appearance and Contour Based Methods for Object Categorization. IEEE Conf. on CVPR, Wisconsin.
  6. Li, J. and Allinson, N. M. (2008). A Comprehensive Review of Current Local Features for Computer Vision. Neurocomputing, 71, 1771-1787.
  7. Lowe, D. G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. IJCV: 60, 91-110.
  8. Mikolajczyk, K., Leibe, B. and Schiele, B. (2006). Multiple Object Class Detection with a Generative Model. IEEE Conf. on CVPR, 26 - 36
  9. Mikolajczyk, K. and Schmid, C. (2005). A Performance Evaluation of Local Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence: 27, 1615-1630.
  10. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T. and Gool, L. V. (2005). A Comparison of Affine Region Detectors IJCV: 65, 43-72
  11. Mindru, F., Tuytelaars, T., Gool, L. V. and Moons, T. (2004). Moment Invariants for Recognition under Changing Viewpoint and Illumination. Computer Vision and Image Understanding: 94, 3-27.
  12. Reiss, T. H. (1993). Recognizing Planar Objects Using Invariant Image Features, Springer-Verlag.
  13. Stark, M. and Schiele, B. (2007). How Good Are Local Features for Classes of Geometric Objects. IEEE 11th International Conference on Computer Vision, 1 - 8.
  14. Zhang, H., Berg, A. C., Maire, M. and Malik, J. (2006). Svm-Knn: Discriminative Nearest Neighbor Classification for Visual Category Recognition. IEEE Conf. on CVPR, 2126- 2136.
  15. Zhang, J. and Marszalek, M. (2006). Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study. IJCV: 73, 213 - 238.
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Paper Citation


in Harvard Style

Islam S. and Sluzek A. (2010). AN EVALUATION OF LOCAL IMAGE FEATURES FOR OBJECT CLASS RECOGNITION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 519-523. DOI: 10.5220/0002844805190523


in Bibtex Style

@conference{visapp10,
author={Saiful Islam and Andrzej Sluzek},
title={AN EVALUATION OF LOCAL IMAGE FEATURES FOR OBJECT CLASS RECOGNITION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={519-523},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002844805190523},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - AN EVALUATION OF LOCAL IMAGE FEATURES FOR OBJECT CLASS RECOGNITION
SN - 978-989-674-029-0
AU - Islam S.
AU - Sluzek A.
PY - 2010
SP - 519
EP - 523
DO - 10.5220/0002844805190523