A performance evaluation of local descriptors
Open accessAbstract
In this paper, we compare the performance of descriptors computed for local interest regions, as, for example, extracted by the Harris-Affine detector [Mikolajczyk, K and Schmid, C, 2004]. Many different descriptors have been proposed in the literature. It is unclear which descriptors are more appropriate and how their performance depends on the interest region detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector. Our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations. We compare shape context [Belongie, S, et al., April 2002], steerable filters [Freeman, W and Adelson, E, Setp. 1991], PCA-SIFT [Ke, Y and Sukthankar, R, 2004], differential invariants [Koenderink, J and van Doorn, A, 1987], spin images [Lazebnik, S, et al., 2003], SIFT [Lowe, D. G., 1999], complex filters [Schaffalitzky, F and Zisserman, A, 2002], moment invariants [Van Gool, L, et al., 1996], and cross-correlation for different types of interest regions. We also propose an extension of the SIFT descriptor and show that it outperforms the original method. Furthermore, we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best. Moments and steerable filters show the best performance among the low dimensional descriptors.
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GLOH is more distinctive than SIFT with the same number of dimensions but is computationally more expensive.
“In the same paper [8], the authors have proposed a variant of SIFT, called GLOH, which proved to be even more distinctive with the same number of dimensions. However, GLOH is computationally more expensive.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [8]
Multimodal fusion combines sources such as vision, language, depth, LiDAR, and speech to enhance scene semantic understanding.
“It combines various sources of information, such as vision, language, depth data, lidar, and speech, to enhance the capability of scene semantic understanding [35,36,37].”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [35]
Hessian-based detectors are more stable and repeatable than their Harris-based counterparts.
“By studying the existing detectors and from published comparisons [15, 8], we can conclude that (1) Hessian-based detectors are more stable and repeatable than their Harris-based counterparts.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [8]
Using the determinant of the Hessian matrix rather than its trace (the Laplacian) is advantageous, as it fires less on elongated, ill-localised structures.
“Using the determinant of the Hessian matrix rather than its trace (the Laplacian) seems advantageous, as it fires less on elongated, ill-localised structures.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [8]
PCA-SIFT yields a 36-dimensional descriptor that is fast for matching but less distinctive than SIFT, and slower feature computation reduces the effect of fast matching.
“This PCA-SIFT yields a 36dimensional descriptor which is fast for matching, but proved to be less distinctive than SIFT in a second comparative study by Mikolajczyk et al. [8] and slower feature computation reduces the effect of fast matching.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [8]
Approximations like the DoG can bring speed at a low cost in terms of lost accuracy.
“Also, (2) approximations like the DoG can bring speed at a low cost in terms of lost accuracy.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [8]
Handcrafted local invariant features gained tremendous popularity, starting from SIFT, and progress on various visual recognition tasks was based substantially on local descriptors.
“Handcrafted local invariant features gained tremendous popularity, starting from the Scale Invariant Feature Transform (SIFT) feature (Lowe 1999), and the progress on various visual recognition tasks was based substantially on the use of local descriptors (Mikolajczyk and Schmid 2005) such as Haarlike features (Viola and Jones 2001), SIFT (Lowe 2004), Shape Contexts (Belongie et al. 2002), Hist”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Mikolajczyk and Schmid 2005
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- Authors
- Mikolajczyk, K., & Schmid, C.
- Affiliations
- University of Oxford (GB)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2005-10
- Volume
- 27
- Issue
- 10
- Pages
- 1615-1630
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2177274842
- Semantic Scholar
- 69401bfdafab7cde00bb8e5b2f6c28e9d72d8cfb
- Cited by
- 6,723
- References
- 52
- Citations by year
- 2026: 44 · 2025: 98 · 2024: 99 · 2023: 144 · 2022: 192 · 2021: 220 · 2020: 203 · 2019: 252 · 2018: 308 · 2017: 393 · 2016: 468 · 2015: 502 · 2014: 534 · 2013: 580 · 2012: 496
- FWCI
- 288.51
- Type
- journal article
- Language
- en
- Open access
- green
- inria.hal.science
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (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
- local descriptors, SIFT, PCA-SIFT, shape context, steerable filters, spin images, moment invariants, differential invariants, complex filters, cross correlation function, descriptor robustness, image transformation
- MeSH terms
- Algorithms, Artificial Intelligence, Computer Simulation, Data Interpretation, Statistical, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Software, Software Validation, Models, Statistical, Information Storage and Retrieval
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