Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Video Sequences Association for People Re-identification across Multiple Non-overlapping Cameras
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
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
A multiple component matching framework for person re-identification
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Fast person re-identification based on dissimilarity representations
Pattern Recognition Letters
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Person re-identification is the task of recognizing an individual that has already been observed over a network of videosurveillance cameras. Methods proposed in literature so far addressed this issue as a classical matching problem: a descriptor is built directly from the view of the person, and a similarity measure between descriptors is defined accordingly. In this work, we propose a general dissimilarity framework for person re-identification, aimed at transposing a generic method for person re-identification based to the commonly adopted multiple instance representation, into a dissimilarity form. Individuals are thus represented by means of dissimilarity values, in respect to common prototypes. Dissimilarity representations carry appealing advantages, in particular the compactness of the resulting descriptor, and the extremely low time required to match two descriptors. Moreover, a dissimilarity representation enables various new applications, some of which are depicted in the paper. An experimental evaluation of the proposed framework applied to an existing method is provided, which clearly shows the advantages of dissimilarity representations in the context of person re-identification.