Image Classification with the Fisher Vector: Theory and Practice
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FV is able to obtain a high discriminative subspace with complementary feature subspace exploration.
“Compared with the conventional methods, FV is able to obtain high discriminative subspace with the complementary feature subspace exploration.”
From Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector (2015) · cites this paper as [23]
The Introduction says FV incorporates additional gradients that improve classification accuracy, and Section 2.5 says it contains more information than BoV by including gradients for means and standard deviations. These passages support the comparison with conventional representations, but they do not establish that FV obtains a discriminative subspace through complementary feature-subspace exploration. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSIFT achieves superior performance compared to other descriptors such as color, especially in the computer vision field.
“It is known that the widely used SIFT23 achieves superior performance compared to other descriptors such as color especially in the computer vision field.”
From Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector (2015) · cites this paper as [23]
The Introduction identifies SIFT as a commonly used image descriptor, and the reported PASCAL VOC results show higher accuracy for the SIFT-only Fisher Vector than for the LCS-only (color-statistics) Fisher Vector (61.8 versus 52.6). Those results support superiority in that particular comparison, not the broader claim that SIFT generally outperforms other descriptors across computer vision. (LLM, conf=0.85)
Experimental — accuracy not yet measured
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- Authors
- Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J.
- Affiliations
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Xerox (France) (FR)
- University of Amsterdam (NL)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Corresponding author
- Sánchez, Jorge
- Venue
- International Journal of Computer Vision
- Published
- 2013-06-12
- Volume
- 105
- Issue
- 3
- Pages
- 222-245
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W1966385142
- Cited by
- 1,498
- References
- 85
- Citations by year
- 2026: 11 · 2025: 20 · 2024: 45 · 2023: 58 · 2022: 102 · 2021: 95 · 2020: 128 · 2019: 164 · 2018: 167 · 2017: 211 · 2016: 224 · 2015: 167 · 2014: 91 · 2013: 13 · 2012: 1
- FWCI
- 171.88
- Type
- journal article
- Language
- en
- Open access
- green
- handle.uba.uva.nl
- Licence
- www.springer.com/tdm (tdm)
- Locations
- International Journal of Computer Vision (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion)
- HAL (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (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 › Advanced Neural Network Applications
- Keywords
- Fisher vectors, Fisher kernel, image classification, bag of visual words, product quantization, image retrieval, feature encoding, linear classifiers
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