Cited by 7,916Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification TechniquesAdvanced Neural Network Applications

Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

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Lazebnik, S., Schmid, C., Ponce, J.·2006·2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)

Abstract

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba’s "gist" and Lowe’s SIFT descriptors.

Integrity signals

Registry

Crossref · 10.1109/cvpr.2006.68

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  • The full-image view is methodologically consistent with traditional methods where encoded SIFT vectors of the entire image are pooled together.

    “Second, the full-image view is methodologically consistent with the traditional methods [15], [17], [19] where the encoded SIFT vectors of the entire image are pooled together.”

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Authors
Lazebnik, S., Schmid, C., & Ponce, J.
Affiliations
  • University of Illinois Urbana-Champaign (US)
  • Centre Inria de l'Université Grenoble Alpes (FR)
Venue
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
Publisher
IEEE
Published
2006-07-10
Volume
2
Pages
2169-2178
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2162915993
Cited by
7,916
References
30
Citations by year
2026: 34 · 2025: 92 · 2024: 118 · 2023: 155 · 2022: 190 · 2021: 266 · 2020: 334 · 2019: 406 · 2018: 493 · 2017: 600 · 2016: 746 · 2015: 872 · 2014: 924 · 2013: 743 · 2012: 541
FWCI
149.57
Type
conference paper
Language
en
Open access
Licence
other-oa
Locations
  • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (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 › Advanced Neural Network Applications
Keywords
scene classification, spatial pyramid matching, bag of visual words, geometric correspondence, image segmentation, Caltech-101, gastrointestinal stromal tumor, SIFT descriptors, image representation

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