An empirical study of context in object detection
Top Cited Papers
- 1 June 2009
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 10636919,p. 1271-1278
- https://doi.org/10.1109/cvpr.2009.5206532
Abstract
This paper presents an empirical evaluation of the role of context in a contemporary, challenging object detection task - the PASCAL VOC 2008. Previous experiments with context have mostly been done on home-grown datasets, often with non-standard baselines, making it difficult to isolate the contribution of contextual information. In this work, we present our analysis on a standard dataset, using top-performing local appearance detectors as baseline. We evaluate several different sources of context and ways to utilize it. While we employ many contextual cues that have been used before, we also propose a few novel ones including the use of geographic context and a new approach for using object spatial support.Keywords
This publication has 23 references indexed in Scilit:
- Object categorization using co-occurrence, location and appearancePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- IM2GPS: estimating geographic information from a single imagePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Putting Objects in PerspectiveInternational Journal of Computer Vision, 2008
- An Interior-Point Method for Large-Scale -Regularized Least SquaresIEEE Journal of Selected Topics in Signal Processing, 2007
- LabelMe: A Database and Web-Based Tool for Image AnnotationInternational Journal of Computer Vision, 2007
- Recovering Surface Layout from an ImageInternational Journal of Computer Vision, 2007
- Recovering Occlusion Boundaries from a Single ImagePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- A hierarchical field framework for unified context-based classificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Probabilistic spatial context models for scene content understandingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Contextual Priming for Object DetectionInternational Journal of Computer Vision, 2003