Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes

Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes (2003)

Cached

Download Links

by Kevin Murphy , Antonio Torralba , William T. Freeman

Citations:

BibTeX

@MISC{Murphy03usingthe,
    author = {Kevin Murphy and Antonio Torralba and William T. Freeman},
    title = {Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes},
    year = {2003}
}

Years of Citing Articles

Bookmark

OpenURL

Abstract

Standard approaches to object detection focus on local patches of the image, and try to classify them as background or not. We propose to use the scene context (image as a whole) as an extra source of (global) information, to help resolve local ambiguities. We present a conditional random field for jointly solving the tasks of object detection and scene classification.

Citations

2308	F C N. Conditional random fields: Probabilistic models for segmenting and labeling sequence data - Lafferty, McCallum, et al.
2046	Rapid object detection using a boosted cascade of simple features - Viola, Jones
1216	Additive logistic regression: A statistical view of boosting - Friedman, Hastie, et al. - 2000
868	Object class recognition by unsupervised scale-invariant learning - Fergus, Perona, et al. - 2003
699	Probabilistic outputs for support vector machines - Platt - 1999
606	Relations between the statistics of natural images and the response properties of cortical cells - Field - 1987
562	Greedy Function Approximation: A Gradient Boosting Machine - Friedman - 2001
485	BoosTexter: a boosting-based system for text categorization - Schapire, Singer - 2000
442	Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary - Duygulu, Barnard, et al.
319	The boosting approach to machine learning: an overview - Schapire - 2003
242	Learning a sparse representation for object detection - Agarwal, Roth - 2002
233	Context-based vision system for place and object recognition - Torralba, Murphy, et al. - 2003
226	A trainable system for object detection - Papageorgiou, Poggio
193	Contextual Priming for Object Detection - Torralba - 2003
182	Discriminative Random Fields: A Discriminative Framework for Contextual Interaction - Kumar, Hebert - 2003
131	Empirical analysis of detection cascades of boosted classifiers for rapid object detection - Lienhart, Kuranov, et al. - 2003
70	Probabilistic spatial context models for scene content understanding. CVPR - Singhal, Luo, et al. - 2003
69	On the semantic of a glance at a scene - Biederman - 1981
69	Contextbased vision: recognizing objects using information from both 2-D and 3-D imagery - Strat, Fischler - 1991
21	Decision making in context - Haralick - 1983
17	before the trees: the precedence of global features in visual perception - Forest - 1977
16	Detecting faces in impoverished images - Torralba, Sinha - 2001
10	Learning multi-label semantic scene classification - Boutell, Shen, et al. - 2004
6	Mutual boosting for contextual influence - Fink, Perona - 2003
4	Robust real-time object detection, International Journal of Computer Vision (2004) 137–154. Please cite this article in press as: N.A. Thacker et al., Performance characterization in computer vision: A guide ..., Comput. Vis. Image Understand - Viola, Jones - 2007