Person re-identification post-rank optimization via hypergraph-based learning

In computer vision, person re-identification has recently received significant attention from researchers and is becoming an emerging research domain with various challenges. Specifically, re-ranking or post-rank optimization is a significant challenge. Existing re-identification methods perform well in certain particular scenarios, but their performance at rank-1 remains a major concern. Such methods cannot model the complex and higher-order relationship among the images. To address such issues, we present a hypergraph-based learning scheme that not only improves the rank-1 accuracy but also models the complex and higher-order relationships among the images. After obtaining the rank list using a baseline method, we apply a new refinement algorithm on it to classify ranks accordingly. Furthermore, to discover the relationship among samples, we utilize the hypergraphs for re-rank learning. A soft assignment technique is used to perform weight learning of hyperedges. The proposed method achieves better ranking performance; consequently, the re-identification is improved. An extensive experimental analysis on challenging and publicly available datasets reveals that the proposed re-ranking scheme performs better than the existing methods.