Person Re-identification by Multi-hypergraph Fusion

• Published in: IEEE transaction on neural networks and learning systems Vol. 28; no. 11; pp. 2763 - 2774
• Main Authors: An, Le, Chen, Xiaojing, Yang, Songfan, Li, Xuelong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Biometric Identification - methods; Cameras; Feature extraction; Feature fusion; graph learning; Graphs; Humans; hypergraph; Image color analysis; Information processing; Matching; Measurement; Neural Networks (Computer); Optimization; Pattern Recognition, Automated - methods; person re-identification; Photography; Probes; Surveillance; Visualization; Measurement; Visualization; Image color analysis; Surveillance; Cameras; Feature extraction; Probes
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2016.2602082

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.