A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Learning Discriminative Appearance-Based Models Using Partial Least Squares
SIBGRAPI '09 Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing
Person re-identification based on global color context
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
International Journal of Computer Vision
Multiple-shot person re-identification by chromatic and epitomic analyses
Pattern Recognition Letters
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Multiple-shot human re-identification by Mean Riemannian Covariance Grid
AVSS '11 Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance
An online learned CRF model for multi-target tracking
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In this article we introduce the problem of identity inference as a generalization of the re-identification problem. Identity inference is applicable in situations where a large number of unknown persons must be identified without knowing a priori that groups of test images represent the same individual. Standard single- and multi-shot person re-identification are special cases of our formulation. We present an approach to solving identity inference problems using a Conditional Random Field (CRF) to model identity inference as a labeling problem in the CRF. The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space, and is flexible enough to incorporate constraints in the temporal and spatial domains. Experimental results are given on the ETHZ dataset. Our approach yields state-of-the-art performance for the multi-shot re-identification task and promising results for more general identity inference problems.