Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Person Reidentification Using Spatiotemporal Appearance
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
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
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Learning Discriminative Appearance-Based Models Using Partial Least Squares
SIBGRAPI '09 Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing
Improving the fisher kernel for large-scale image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Person Re-identification Using Haar-based and DCD-based Signature
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Pedestrian recognition with a learned metric
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Gabor-LBP Based Region Covariance Descriptor for Person Re-identification
ICIG '11 Proceedings of the 2011 Sixth International Conference on Image and Graphics
Person re-identification by probabilistic relative distance comparison
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
PCCA: A new approach for distance learning from sparse pairwise constraints
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Editor's Choice Article: A survey of approaches and trends in person re-identification
Image and Vision Computing
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This paper proposes a new descriptor for person re-identification building on the recent advances of Fisher Vectors. Specifically, a simple vector of attributes consisting in the pixel coordinates, its intensity as well as the first and second-order derivatives is computed for each pixel of the image. These local descriptors are turned into Fisher Vectors before being pooled to produce a global representation of the image. The so-obtained Local Descriptors encoded by Fisher Vector (LDFV) have been validated through experiments on two person re-identification benchmarks (VIPeR and ETHZ), achieving state-of-the-art performance on both datasets.