Co-Metric Learning for Person Re-Identification
Person re-identification, aiming to identify the same pedestrian images across disjoint camera views, is a key technique of intelligent video surveillance. Although existing methods have developed both theories and experimental results, most of effective ones pertain to fully supervised training sty...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2018/3586191 |
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| _version_ | 1849694618770210816 |
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| author | Qingming Leng |
| author_facet | Qingming Leng |
| author_sort | Qingming Leng |
| collection | DOAJ |
| description | Person re-identification, aiming to identify the same pedestrian images across disjoint camera views, is a key technique of intelligent video surveillance. Although existing methods have developed both theories and experimental results, most of effective ones pertain to fully supervised training styles, which suffer the small sample size (SSS) problem a lot, especially in label-insufficient practical applications. To bridge SSS problem and learning model with small labels, a novel semisupervised co-metric learning framework is proposed to learn a discriminative Mahalanobis-like distance matrix for label-insufficient person re-identification. Different from typical co-training task that contains multiview data originally, single-view person images are firstly decomposed into pseudo two views, and then metric learning models are produced and jointly updated based on both pseudo-labels and references iteratively. Experiments carried out on three representative person re-identification datasets show that the proposed method performs better than state of the art and possesses low label sensitivity. |
| format | Article |
| id | doaj-art-a5e8c77bba7b4e61aa9660f51c3ec03d |
| institution | DOAJ |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-a5e8c77bba7b4e61aa9660f51c3ec03d2025-08-20T03:20:00ZengWileyAdvances in Multimedia1687-56801687-56992018-01-01201810.1155/2018/35861913586191Co-Metric Learning for Person Re-IdentificationQingming Leng0School of Information Science and Technology, Jiujiang University, Jiujiang 332000, ChinaPerson re-identification, aiming to identify the same pedestrian images across disjoint camera views, is a key technique of intelligent video surveillance. Although existing methods have developed both theories and experimental results, most of effective ones pertain to fully supervised training styles, which suffer the small sample size (SSS) problem a lot, especially in label-insufficient practical applications. To bridge SSS problem and learning model with small labels, a novel semisupervised co-metric learning framework is proposed to learn a discriminative Mahalanobis-like distance matrix for label-insufficient person re-identification. Different from typical co-training task that contains multiview data originally, single-view person images are firstly decomposed into pseudo two views, and then metric learning models are produced and jointly updated based on both pseudo-labels and references iteratively. Experiments carried out on three representative person re-identification datasets show that the proposed method performs better than state of the art and possesses low label sensitivity.http://dx.doi.org/10.1155/2018/3586191 |
| spellingShingle | Qingming Leng Co-Metric Learning for Person Re-Identification Advances in Multimedia |
| title | Co-Metric Learning for Person Re-Identification |
| title_full | Co-Metric Learning for Person Re-Identification |
| title_fullStr | Co-Metric Learning for Person Re-Identification |
| title_full_unstemmed | Co-Metric Learning for Person Re-Identification |
| title_short | Co-Metric Learning for Person Re-Identification |
| title_sort | co metric learning for person re identification |
| url | http://dx.doi.org/10.1155/2018/3586191 |
| work_keys_str_mv | AT qingmingleng cometriclearningforpersonreidentification |