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2014 IEEE Winter Conference on Applications of Computer Vision (WACV) (2014)
Steamboat Springs, CO, USA
March 24, 2014 to March 26, 2014
ISBN: 978-1-4799-4985-4
pp: 721-728
Xi Chen , University of Maryland College Park, 20740, USA
Arpit Jain , University of Maryland College Park, 20740, USA
Larry S. Davis , University of Maryland College Park, 20740, USA
ABSTRACT
We introduce a new problem called object co-labeling where the goal is to jointly annotate multiple images of the same scene which do not have temporal consistency. We present an adaptive framework for joint segmentation and recognition to solve this problem. We propose an objective function that considers not only appearance but also appearance and context consistency across images of the scene. A relaxed form of the cost function is minimized using an efficient quadratic programming solver. Our approach improves labeling performance compared to labeling each image individually. We also show the application of our co-labeling framework to other recognition problems such as label propagation in videos and object recognition in similar scenes. Experimental results demonstrates the efficacy of our approach.
INDEX TERMS
Image segmentation, Buildings, Cost function, Labeling, Videos, Silicon, Image edge detection
CITATION

X. Chen, A. Jain and L. S. Davis, "Object co-labeling in multiple images," 2014 IEEE Winter Conference on Applications of Computer Vision (WACV), Steamboat Springs, CO, USA, 2014, pp. 721-728.
doi:10.1109/WACV.2014.6836031
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