Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Person Reidentification Using Spatiotemporal Appearance
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Multiple Component Learning for Object Detection
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
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
Exploiting dissimilarity representations for person re-identification
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Appearance-based people recognition by local dissimilarity representations
Proceedings of the on Multimedia and security
Fast person re-identification based on dissimilarity representations
Pattern Recognition Letters
A general method for appearance-based people search based on textual queries
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Editor's Choice Article: A survey of approaches and trends in person re-identification
Image and Vision Computing
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Person re-identification consists in recognizing an individual that has already been observed over a network of cameras. It is a novel and challenging research topic in computer vision, for which no reference framework exists yet. Despite this, previous works share similar representations of human body based on part decomposition and the implicit concept of multiple instances. Building on these similarities, we propose a Multiple Component Matching (MCM) framework for the person reidentification problem, which is inspired by Multiple Component Learning, a framework recently proposed for object detection [3]. We show that previous techniques for person re-identification can be considered particular implementations of our MCM framework. We then present a novel person re-identification technique as a direct, simple implementation of our framework, focused in particular on robustness to varying lighting conditions, and show that it can attain state of the art performances.