title: Person Re-identification by Multi-hypergraph Fusion.
abstract: Matching people across nonoverlapping cameras, also known as person re-identification, is an important and challenging research topic. Despite its great demand in many crucial applications such as surveillance, person re-identification is still far from being solved. Due to drastic view changes, even the same person may look quite dissimilar in different cameras. Illumination and pose variations further aggravate this discrepancy. To this end, various feature descriptors have been designed for improving the matching accuracy. Since different features encode information from different aspects, in this paper, we propose to effectively leverage multiple off-the-shelf features via multi-hypergraph fusion. A hypergraph captures not only pairwise but also high-order relationships among the subjects being matched. In addition, different from conventional approaches in which the matching is achieved by computing the pairwise distance or similarity between a probe and a gallery subject, the similarities between the probe and all gallery subjects are learned jointly via hypergraph optimization. Experiments on popular data sets demonstrate the effectiveness of the proposed method, and a superior performance is achieved as compared with the most recent state-of-the-arts.Matching people across nonoverlapping cameras, also known as person re-identification, is an important and challenging research topic. Despite its great demand in many crucial applications such as surveillance, person re-identification is still far from being solved. Due to drastic view changes, even the same person may look quite dissimilar in different cameras. Illumination and pose variations further aggravate this discrepancy. To this end, various feature descriptors have been designed for improving the matching accuracy. Since different features encode information from different aspects, in this paper, we propose to effectively leverage multiple off-the-shelf features via multi-hypergraph fusion. A hypergraph captures not only pairwise but also high-order relationships among the subjects being matched. In addition, different from conventional approaches in which the matching is achieved by computing the pairwise distance or similarity between a probe and a gallery subject, the similarities between the probe and all gallery subjects are learned jointly via hypergraph optimization. Experiments on popular data sets demonstrate the effectiveness of the proposed method, and a superior performance is achieved as compared with the most recent state-of-the-arts.
authors.affiliation: National Key Laboratory of Science and Technology on Multi-spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Le
authors.initials: L
authors.lastname: An
authors.affiliation: Department of Computer Science and Engineering, University of California, Riverside, CA, USA.
authors.forename: Xiaojing
authors.initials: X
authors.lastname: Chen
authors.affiliation: College of Electronics and Information Engineering, Sichuan University, Chengdu, China.
authors.forename: Songfan
authors.initials: S
authors.lastname: Yang
authors.affiliation: Chinese Academy of Sciences, Institute of Optics and Precision Mechanics, Xi'an, China.
authors.forename: Xuelong
authors.initials: X
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D056667:Biometric Identification / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography
keywords: Cameras; Feature extraction; Image color analysis; Measurement; Probes; Surveillance; Visualization