The Effect of SIFT Features Properties in Descriptors Matching for Near-duplicate Retrieval Tasks

Afra'a Ahmad Alyosef, Andreas Nürnberger

Abstract

The scale invariant feature transformation algorithm (SIFT) has been widely used for near-duplicate retrieval tasks. Most studies and evaluations published so far focused on increasing retrieval accuracy by improving descriptor properties and similarity measures. Contrast, scale and orientation properties of the SIFT features were used in computing the SIFT descriptor, but their explicit influence in the feature matching step was not studied. Moreover, it has not been studied yet how to specify an appropriate criterion to extract (almost) the same number of SIFT features (respectively keypoints) of all images in a database. In this work, we study the effects of contrast and scale properties of SIFT features when ranking and truncating the extracted descriptors. In addition, we evaluate if scale, contrast and orientation features can be used to bias the descriptor matching scores, i.e., if the keypoints are quite similar in these features, we enforce a higher similarity in descriptor matching. We provide results of a benchmark data study using the proposed modifications in the original SIFT􀀀128D and on the region compressed SIFT (RC-SIFT􀀀64D) descriptors. The results indicate that using contrast and orientation features to bias feature matching can improve near-duplicate retrieval performance.

References

  1. Aly, M., Welinder, P., Munich, M., and Perona, P. (2011). Caltech-buildings benchmark. In Available at http:// www.vision.caltech.edu/malaa/datasets/caltechbuildings/.
  2. Alyosef, A. A. and N ürnberger, A. (2016). Adapted sift descriptor for improved near duplicate retrieval. In Proc. of the 5th International Conference on Pattern Recognition Applications and Methods, pages 55-64.
  3. Auclair, A., Vincent, N., and Cohen, L. (2006). Hash functions for near duplicate image retrieval. In In WACV, pages 7-8.
  4. Chum, O., Philbin, J., and Zisserman, A. (2008). Near duplicate image detection: min-hash and tf-idf weighting. In British Machine Vision Conference.
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Paper Citation


in Harvard Style

Ahmad Alyosef A. and Nürnberger A. (2017). The Effect of SIFT Features Properties in Descriptors Matching for Near-duplicate Retrieval Tasks . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 703-710. DOI: 10.5220/0006250607030710


in Bibtex Style

@conference{icpram17,
author={Afra'a Ahmad Alyosef and Andreas Nürnberger},
title={The Effect of SIFT Features Properties in Descriptors Matching for Near-duplicate Retrieval Tasks},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={703-710},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006250607030710},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - The Effect of SIFT Features Properties in Descriptors Matching for Near-duplicate Retrieval Tasks
SN - 978-989-758-222-6
AU - Ahmad Alyosef A.
AU - Nürnberger A.
PY - 2017
SP - 703
EP - 710
DO - 10.5220/0006250607030710