Abstract
An incremental attribute discovery method for person re-identification is proposed in this paper. Recent studies have shown the effectiveness of the attribute-based approach. Unfortunately, the approach has difficulty in discriminating people who are similar in terms of the pre-defined semantic attributes. To solve this problem, we automatically discover and learn new attributes that permit successful discrimination through a pair-wise learning process. We evaluate our method on two benchmark datasets and demonstrate that it significantly improves the performance of the person re-identification task.
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Umeda, T., Sun, Y., Irie, G., Sudo, K., Kinebuchi, T. (2016). Attribute Discovery for Person Re-Identification. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_24
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DOI: https://doi.org/10.1007/978-3-319-27674-8_24
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