Indexing based on scale invariant interest points
Open accessAbstract
This paper presents a new method for detecting scale invariant interest points. The method is based on two recent results on scale space: (1) Interest points can be adapted to scale and give repeatable results (geometrically stable). (2) Local extrema over scale of normalized derivatives indicate the presence of characteristic local structures. Our method first computes a multi-scale representation for the Harris interest point detector. We then select points at which a local measure (the Laplacian) is maximal over scales. This allows a selection of distinctive points for which the characteristic scale is known. These points are invariant to scale, rotation and translation as well as robust to illumination changes and limited changes of viewpoint. For indexing, the image is characterized by a set of scale invariant points; the scale associated with each point allows the computation of a scale invariant descriptor. Our descriptors are, in addition, invariant to image rotation, of affine illumination changes and robust to small perspective deformations. Experimental results for indexing show an excellent performance up to a scale factor of 4 for a database with more than 5000 images.
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The detector is based on the Hessian matrix because of its good performance in computation time and accuracy.
“We base our detector on the Hessian matrix because of its good performance in computation time and accuracy.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [11]
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Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472 · see citing works
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SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · see citing works
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · see citing works
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723 · see citing works
Self-citationMachine Learning for High-Speed Corner Detection
Rosten, E., & Drummond, T. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_34 · Cited by 4,555 · see citing works
Scale & Affine Invariant Interest Point Detectors
Mikolajczyk, K., & Schmid, C. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000027790.02288.f2 · Cited by 3,436 · see citing works
Self-citationPCA-SIFT: a more distinctive representation for local image descriptors
Ke, Y., & Sukthankar, R. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315206 · Cited by 3,237 · see citing works
A comparison of affine region detectors
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Gool, L. V. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-3848-x · Cited by 2,939 · see citing works
Self-citationRobust Wide Baseline Stereo from Maximally Stable Extremal Regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2002) · Procedings of the British Machine Vision Conference 2002 · doi:10.5244/c.16.36 · Cited by 1,350 · see citing works
An Affine Invariant Interest Point Detector
Mikolajczyk, K., & Schmid, C. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-47969-4_9 · Cited by 1,325 · see citing works
Self-citationVisual Place Recognition: A Survey
Lowry, S., Sünderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., & Milford, M. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2496823 · Cited by 1,079 · see citing works
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211478 · Cited by 955 · see citing works
Self-citationInvariant Features from Interest Point Groups
Brown, M., & Lowe, D. (2002) · Procedings of the British Machine Vision Conference 2002 · doi:10.5244/c.16.23 · Cited by 611 · see citing works
Grabner, M., Grabner, H., & Bischof, H. (2006) · Lecture Notes in Computer Science · doi:10.1007/11612032_92 · Cited by 208 · see citing works
Grauman, K., & Leibe, B. (2011) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00332ed1v01y201103aim011 · Cited by 126 · see citing works
Multi-scale phase-based local features
Carneiro, G., & Jepson, A. D. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211426 · Cited by 104 · see citing works
Real-Time Scale Selection in Hybrid Multi-scale Representations
Lindeberg, T., & Bretzner, L. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-44935-3_11 · Cited by 85 · see citing works
- Authors
- Mikolajczyk, K., & Schmid, C.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Venue
- Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-13
- Volume
- 1
- Pages
- 525-531
- OpenAlex
- W2119747362
- Cited by
- 1,101
- References
- 1
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 9 · 2023: 13 · 2022: 10 · 2021: 22 · 2020: 26 · 2019: 31 · 2018: 49 · 2017: 44 · 2016: 53 · 2015: 56 · 2014: 80 · 2013: 65 · 2012: 68
- Type
- paratext
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- other-oa
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
- Keywords
- image indexing, scale space, Harris corner detector, multi-scale representation, Laplacian, rotation invariance, illumination invariance, perspective invariance, image descriptors, image retrieval
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