Cited by 355Advanced Image and Video Retrieval TechniquesAdvanced Neural Network ApplicationsVisual Attention and Saliency Detection

Context based object categorization: A critical survey

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Galleguillos, Carolina, Belongie, Serge·2010·Computer Vision and Image Understanding

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  • Context can broadly be grouped into three categories: semantic context, spatial context, and scale context.

    “Many different types of context have been discussed (Divvala et al. 2009;Galleguillos and Belongie 2010), and can broadly be grouped into one of three categories: 1. Semantic context: The likelihood of an object to be found in some scenes, but not in others; 2. Spatial context: The likelihood of finding an object in some position and not others with respect to other objects in the scene; 3. Scale”

    From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Galleguillos and Belongie 2010

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1 of the 41 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

  1. A. Hanson and E. Riseman. Visions: A computer vision system for interpreting scenes. Computer Vision Systems, pages 303-334, 1978.
  2. A. Rabinovich, A. Vedaldi, C. Galleguillos, E. Wiewiora, and S. Belongie. Ob- jects in context. Proceedings of the International Conference on Computer Vision (ICCV), 2007.
  3. R. Rensink, J. O'Regan, and J. Clark. The need for attention to perceive changes in scenes. Psychological Science, 8(5):368-373, 1997.
  4. T. Strat and M. Fischler. Context-based vision: Recognizing objects using infor- mation from both 2-d and 3-d imagery. Pattern Analysis and Machine Vision, 13(10):1050-1065, October 1991.

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Authors
Galleguillos, C., & Belongie, S.
Affiliations
  • University of California San Diego (US)
  • California Institute of Technology (US)
Corresponding author
Galleguillos, Carolina
Venue
Computer Vision and Image Understanding
Publisher
Elsevier BV
Published
2010-06
Volume
114
Issue
6
Pages
712-722
ISSN
1077-3142 (ISSN-L)
OpenAlex
W2111868822
Cited by
351
References
41
Citations by year
2026: 6 · 2025: 12 · 2024: 9 · 2023: 8 · 2022: 12 · 2021: 23 · 2020: 26 · 2019: 30 · 2018: 25 · 2017: 27 · 2016: 28 · 2015: 31 · 2014: 35 · 2013: 24 · 2012: 20
FWCI
28.95
Type
journal article
Language
en
Open access
Locations
  • Computer Vision and Image Understanding (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • CaltechAUTHORS (California Institute of Technology) (submittedVersion)
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 › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords
object classification, context modeling, object recognition, contextual information, occlusion, appearance-based recognition, scene statistics, context-aware interaction, scalability

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