Clustering through decision tree construction
Proceedings of the ninth international conference on Information and knowledge management
Machine Learning
Spectral clustering with eigenvector selection
Pattern Recognition
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
Cascade of descriptors to detect and track objects across any network of cameras
Computer Vision and Image Understanding
Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding
International Journal of Computer Vision
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
Person Re-identification Using Spatial Covariance Regions of Human Body Parts
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Person re-identification by descriptive and discriminative classification
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Multiple-shot person re-identification by chromatic and epitomic analyses
Pattern Recognition Letters
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
Large scale metric learning from equivalence constraints
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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)
Tracking multiple people under global appearance constraints
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Set based discriminative ranking for recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Relaxed pairwise learned metric for person re-identification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Person re-identification: what features are important?
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Reidentification by Relative Distance Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with generic weights, which are assumed to be universally and equally good for all individuals, independent of people's different appearances. In this study, we show that certain features play more important role than others under different viewing conditions. To explore this characteristic, we propose a novel unsupervised approach to bottom-up feature importance mining on-the-fly specific to each re-identification probe target image, so features extracted from different individuals are weighted adaptively driven by their salient and inherent appearance attributes. Extensive experiments on three public datasets give insights on how feature importance can vary depending on both the viewing condition and specific person's appearance, and demonstrate that unsupervised bottom-up feature importance mining specific to each probe image can facilitate more accurate re-identification especially when it is combined with generic universal weights obtained using existing distance metric learning methods.