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Context based object categorization: A critical survey
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“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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Metadata
- 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
- green
- escholarship.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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