Name
Title | ||
|---|---|---|
Robust Partial Person Re-identification Based on Similarity-Guided Sparse Representation. |
Abstract | ||
|---|---|---|
In this paper, we study the problem of partial person re-identification (re-id). This problem is more difficult than general person re-identification because the body in probe image is not full. We propose a novel method, similarity-guided sparse representation (SG-SR), as a robust solution to improve the discrimination of the sparse coding. There are three main components in our method. In order to include multi-scale information, a dictionary consisting of features extracted from multi-scale patches is established in the first stage. A low rank constraint is then enforced on the dictionary based on the observation that its subspaces of each class should have low dimensions. After that, a classification model is built based on a novel similarity-guided sparse representation which can choose vectors that are more similar to the probe feature vector. The results show that our method outperforms existing partial person re-identification methods significantly and achieves state-of-the-art accuracy. |
Year | Venue | Field |
|---|---|---|
2017 | CCBR | Feature vector,Pattern recognition,Neural coding,Computer science,Sparse approximation,Linear subspace,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
16 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Ren | 1 | 0 | 1.01 |
| Lingxiao He | 2 | 0 | 0.34 |
| Haiqing Li | 3 | 77 | 7.57 |
| Yunfan Liu | 4 | 4 | 3.43 |
| Zhenan Sun | 5 | 2379 | 139.49 |
| Tieniu Tan | 6 | 11681 | 744.35 |