[Journal Paper] Classification of Local Eigen-Dissimilarities for Person Re-Identification
Title: Classification of Local Eigen-Dissimilarities for Person Re-Identification
Authors : Niki Martinel , Christian Micheloni
Journal: IEEE Signal Processing Letters 22 (4): 455-459, 2015
The task of re-identifying a person that moves across cameras fields-of-view is a challenge to the community known as the person re-identification problem.
State-of-the art approaches are either based on direct modeling and matching of the human appearance or on machine learning-based techniques.
In this work we introduce a novel approach that studies densely localized image dissimilarities in a low dimensional space and uses those to re-identify between persons in a supervised classification framework.
To achieve the goal:
1) we compute the localized image dissimilarity between a pair of images;
2) we learn the lower dimensional space of such localized image dissimilarities, known as the “local eigen-dissimilarities (LEDs) space;
3) we train a binary classifier to discriminate between LEDs computed for a positive pair (images are for a same person) from the ones computed for a negative pair (images are for different persons).
We show the competitive performance of our approach on two publicly available benchmark datasets.