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2015 | OriginalPaper | Buchkapitel

Semi-Supervised Ranking for Re-identification with Few Labeled Image Pairs

verfasst von : Andy Jinhua Ma, Ping Li

Erschienen in: Computer Vision -- ACCV 2014

Verlag: Springer International Publishing

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Abstract

In many person re-identification applications, typically only a small number of labeled image pairs are available for training. To address this serious practical issue, we propose a novel semi-supervised ranking method which makes use of unlabeled data to improve the re-identification performance. It is shown that low density separation or graph propagation assumption is not valid under some conditions in person re-identification. Thus, we propose to iteratively select the most confident matched (positive) image pairs from the unlabeled data. Since the number of positive matches is greatly smaller than that of negative ones, we increase the positive prior by selecting positive data from the top-ranked matching subset among all unlabeled data. The optimal model is learnt by solving a regression based ranking problem. Experimental results show that our method significantly outperforms state-of-the-art distance learning algorithms on three publicly available datasets using only few labeled matched image pairs for training.

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Vorheriges Kapitel Camera Motion and Surrounding Scene Appearance as Context for Action Recognition

Nächstes Kapitel Robust Visual Tracking with Dual Group Structure

Nur mit Berechtigung zugänglich

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https://lrs.icg.tugraz.at/datasets/prid/.

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Note that the feature used in our experiments is different from those in existing methods. It is very discriminative for VIPeR dataset, so it can achieve 70 % rank one accuracy using 316 matched image pairs for training. Such good performance may be due to the combination of foreground detection and global feature extraction (on a large region of an image) which is very effective for VIPeR dataset. It is interesting to conduct further investigation on this issue, but it is not the focus of this paper.

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Titel: Semi-Supervised Ranking for Re-identification with Few Labeled Image Pairs
verfasst von: Andy Jinhua Ma
Ping Li
Verlag: Springer International Publishing
Buch: Computer Vision -- ACCV 2014
Print ISBN: 978-3-319-16816-6

Electronic ISBN: 978-3-319-16817-3

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-16817-3_39

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