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Describing Visual Scenes Using Transformed Objects and Parts

Authors:

Erik B. Sudderth,

Antonio Torralba,

William T. Freeman,

Alan S. WillskyAuthors Info & Claims

International Journal of Computer Vision, Volume 77, Issue 1-3

Pages 291 - 330

https://doi.org/10.1007/s11263-007-0069-5

Published: 01 May 2008 Publication History

Abstract

We develop hierarchical, probabilistic models for objects, the parts composing them, and the visual scenes surrounding them. Our approach couples topic models originally developed for text analysis with spatial transformations, and thus consistently accounts for geometric constraints. By building integrated scene models, we may discover contextual relationships, and better exploit partially labeled training images. We first consider images of isolated objects, and show that sharing parts among object categories improves detection accuracy when learning from few examples. Turning to multiple object scenes, we propose nonparametric models which use Dirichlet processes to automatically learn the number of parts underlying each object category, and objects composing each scene. The resulting transformed Dirichlet process (TDP) leads to Monte Carlo algorithms which simultaneously segment and recognize objects in street and office scenes.

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Index Terms

Describing Visual Scenes Using Transformed Objects and Parts
1. Computing methodologies

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Published In

cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 77, Issue 1-3

May 2008

321 pages

ISSN:0920-5691

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Copyright © Copyright © 2008 Springer Science+Business Media, LLC.

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Kluwer Academic Publishers

United States

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Published: 01 May 2008

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Cited By

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https://dl.acm.org/doi/10.1109/TPAMI.2023.3332435
Li XLei L(2021)A bibliometric analysis of topic modelling studies (2000–2017)Journal of Information Science10.1177/016555151987704947:2(161-175)Online publication date: 23-Mar-2021
https://dl.acm.org/doi/10.1177/0165551519877049
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Zhu HYuen KMihaylova LLeung H(2017)Overview of Environment Perception for Intelligent VehiclesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2017.265866218:10(2584-2601)Online publication date: 29-Sep-2017
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Zhang FMao QDong MZhan YHanjalic ASnoek CWorring MBulterman DHuet BKelliher AKompatsiaris YLi J(2016)Multi-pose Facial Expression Recognition Using Transformed Dirichlet ProcessProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2967240(347-351)Online publication date: 1-Oct-2016
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Tu NDinh DRasel MLee Y(2016)Topic modeling and improvement of image representation for large-scale image retrievalInformation Sciences: an International Journal10.1016/j.ins.2016.05.029366:C(99-120)Online publication date: 20-Oct-2016
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