Artificial Intelligence
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
A Riemannian Framework for Tensor Computing
International Journal of Computer Vision
Covariance Tracking using Model Update Based on Lie Algebra
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Person Reidentification Using Spatiotemporal Appearance
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ViSE: Visual Search Engine Using Multiple Networked Cameras
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Pedestrian Detection via Classification on Riemannian Manifolds
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 Pairwise Dissimilarity Profiles for Appearance Recognition in Visual Surveillance
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Learning Discriminative Appearance-Based Models Using Partial Least Squares
SIBGRAPI '09 Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing
Multiple-Shot Person Re-identification by HPE Signature
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Pedestrian recognition with a learned metric
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Person re-identification by descriptive and discriminative classification
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Editor's Choice Article: A survey of approaches and trends in person re-identification
Image and Vision Computing
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This paper addresses the problem of appearance matching across disjoint camera views. Significant appearance changes, caused by variations in view angle, illumination and object pose, make the problem challenging. We propose to formulate the appearance matching problem as the task of learning a model that selects the most descriptive features for a specific class of objects. Learning is performed in a covariance metric space using an entropy-driven criterion. Our main idea is that different regions of the object appearance ought to be matched using various strategies to obtain a distinctive representation. The proposed technique has been successfully applied to the person re-identification problem, in which a human appearance has to be matched across non-overlapping cameras. We demonstrate that our approach improves state of the art performance in the context of pedestrian recognition.