Abstract

Owing to visual ambiguities and disparities, person re-identification methods inevitably produce sub optimal rank-list, which still requires exhaustive human eyeballing to identify the correct target from hundreds of different likely-candidates. Existing re-identification studies focus on improving the ranking performance, but rarely look into the critical problem of optimising the time-consuming and error-prone post-rank visual search at the user end. In this study, we present a novel one-shot Post-rank Optimization (POP) method, which allows a user to quickly refine their search by either "one-shot" or a couple of sparse negative selections during a re-identification process. We conduct systematic behavioural studies to understand user's searching behaviour and show that the proposed method allows correct re-identification to converge 2.6 times faster than the conventional exhaustive search. Importantly, through extensive evaluations we demonstrate that the method is capable of achieving significant improvement over the state-of-the-art distance metric learning based ranking models, even with just "one shot" feedback optimisation, by as much as over 30% performance improvement for rank 1 re-identification on the VIPeR and i-LIDS datasets.

Keywords

Computer scienceIdentification (biology)Rank (graph theory)Metric (unit)Ranking (information retrieval)Learning to rankMachine learningArtificial intelligenceProcess (computing)Data miningInformation retrievalMathematicsEngineering

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Publication Info

Year
2013
Type
article
Pages
441-448
Citations
146
Access
Closed

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Chunxiao Liu, Chen Change Loy, Shaogang Gong et al. (2013). POP: Person Re-identification Post-rank Optimisation. , 441-448. https://doi.org/10.1109/iccv.2013.62

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DOI
10.1109/iccv.2013.62