[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.
hello Martinel, it’s my honour to communicate with you. I’m lufei Chen, a two-year graduate student in China, and i want to reappear this papper’s idea, can you send this papper’s code to me? Thank you verry much, looking forward your letter.