A Comparison of Local Detectors and Descriptors for Multi-Object Applications
Authors: 
Faten A. Khalifa, Noura A. Semary, Hatem M. El-Sayed, Mohiy M. HadhoudAuthors Info & Claims
Article No.: 23, Pages 1 - 5
Abstract
There have been a number of evaluations and comparisons between feature detectors and descriptors and between their different implementations. These detectors and descriptors respond differently depending on the image structure. In this paper, we evaluate the overall performance of the most efficient detectors and descriptors in terms of speed and efficiency. The evaluation is carried out on a set of images of different object classes and structures with different geometric and photometric deformations. This evaluation would be useful for multi-object applications such as digilog books. It has been observed that some detectors perform better with certain object classes. Differences in performance of the descriptors vary with different image structures.
References
[1]
Tuytelaars, T., and Mikolajczyk, K. 2008. Local invariant feature detectors: a survey. Found. Trends. Comput. Graph. Vis. 3, 3, 177--280.
[2]
Lowe, D. 1999. Object recognition from local scale-invariant features. The Proceedings of the Seventh IEEE International Conference on Computer Vision. 2, 1150--1157.
[3]
Bay, H., Ess, A., Tuytelaars, T., and Gool, L. V. 2008. Speeded-up robust features (SURF). Computer Vision and Image Understanding. 110, 3, 346--359.
[4]
Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. 2011. ORB: an efficient alternative to SIFT or SURF. Proceedings of the IEEE International Conference on Computer Vision. 2564--2571.
[5]
Calonder, M., Lepetit, V., Strecha, C., and Fua, P. 2010. BRIEF: binary robust independent elementary features. Proceedings of the 11th European Conference on Computer Vision. 778--792.
[6]
Rosten, E., and Drummond, T. 2006. Machine learning for high-speed corner detection. Proceedings of the 9th European Conference on Computer Vision. 430--443.
[7]
Leutenegger, S., Chli, M., and Siegwart, R. Y. 2011. BRISK: binary robust invariant scalable keypoints. Proceedings of the International Conference on Computer Vision. 2548--2555.
[8]
Mikolajczyk, K., et al. 2005. A Comparison of Affine Region Detectors. International Journal of Computer Vision. 65, 1-2, 43--72.
[9]
Mikolajczyk, K., and Schmid, C. 2005. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27, 10, 1615--1630.
[10]
Juan, L., and Gwon, O. 2009. A comparison of SIFT, PCASIFT and SURF. International Journal of Image Processing. 3, 4, 143--152.
[11]
Miksik, O., and Mikolajczyk, K. 2012. Evaluation of local detectors and descriptors for fast feature matching. Proceedings of the International Conference on Pattern Recognition. 2681--2684.
[12]
Gossow, D., Decker, P., and Paulus, D. 2011. An evaluation of open source SURF implementations. J. Ruiz-del-Solar, E. Chown, and P. Plöger, ed., RoboCup 2010: Robot Soccer World Cup XIV, Springer. 169--179.
[13]
Bauer, J., Sünderhauf, N., and Protzel, P. 2007. Comparing several implementations of two recently published feature detectors. Proceedings of the International Conference on Intelligent and Autonomous Systems.
[14]
Lankinen, J., Kangas, V., and Kamarainen, J.-K. 2012. A comparison of local feature detectors and descriptors for visual object categorization by intra-class repeatability and matching. International Conference in Pattern Recognition.
[15]
Fischler, M. A., and Bolles, R. C. 1981. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM. 24, 6, 381--395.
[16]
Deng, J., et al. 2009. Imagenet: a large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition. 248--255.
[17]
Bradski, G. 2010. The opencv library. Doctor Dobbs Journal. 25,11,120--126.
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- A Comparison of Local Detectors and Descriptors for Multi-Object Applications
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Published In
November 2015
495 pages
ISBN:9781450334587
DOI:10.1145/2816839
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Association for Computing Machinery
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Published: 23 November 2015
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IPAC '15
IPAC '15: International Conference on Intelligent Information Processing, Security and Advanced Communication
November 23 - 25, 2015
Batna, Algeria
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Overall Acceptance Rate 87 of 367 submissions, 24%
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