Person Re-Identification by Weighted Integration of Sparse and Collaborative Representation

Person Re-Identification by Weighted Integration of Sparse and Collaborative Representation

Blog Article

Recognizing the certain person of interest in cameras of different viewpoints is cystorelin 100ml known as the task of person re-identification.It has been a challenging job considering the variation in human pose, the changing illumination conditions and the lack of paired samples.Previous matching techniques in the person re-identification field mainly focus on Mahalanobis-like metric learning functions.

Taking advantage of the sparse representation and collaborative representation, we propose a new approach that elaborately exploits both the globality and locality of images.First, we explore multi-feature extraction with different spatial levels.The extracted features are then projected to a common subspace which handles dimension reduction.

Second, we learn a single dictionary for each level that is invariant with the changing of viewpoints.Third, we adopt a weighted fusion approach that combines the dictionary learning-based sparse representation with collaborative representation.Experiments on two benchmark re-identification data sets twizzlers orange cream pop filled licorice twists (VIPeR and GRID) justify the advances of our integration algorithm by comparing with several state-of-the-art methods.