Metric Embedded Discriminative Vocabulary Learning for High-Level Person Representation

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	Metric Embedded Discriminative Vocabulary Learning for High-Level Person Representation
	Yang Yang(杨阳); Zhen Lei; Shifeng Zhang; Hailin Shi; Stan Z. Li; Yang Yang
	2016-05
会议名称	Association for the Advancement of Artificial Intelligence
会议录名称	AAAI
会议日期	2016, 02.12-02.17
会议地点	Phoenix, USA
摘要	A variety of encoding methods for bag of word (BoW) model have been proposed to encode the local features in image classification. However, most of them are unsupervised and just employ k-means to form the visual vocabulary, thus reducing the discriminative power of the features. In this paper, we propose a metric embedded discriminative vocabulary learning for high-level person representation with application to person re-identification. A new and effective term is introduced which aims at making the same persons closer while different ones farther in the metric space. With the learned vocabulary, we utilize a linear coding method to encode the image-level features (or holistic image features) for extracting high-level person representation. Different from traditional unsupervised approaches, our method can explore the relationship (same or not) among the persons. Since there is an analytic solution to the linear coding, it is easy to obtain the final high-level features. The experimental results on person re-identification demonstrate the effectiveness of our proposed algorithm.
关键词	Person Re-identification Metric Embedded Discriminative Vocabulary Learning High-level Person Representation
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11851
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
通讯作者	Yang Yang
作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yang Yang,Zhen Lei,Shifeng Zhang,et al. Metric Embedded Discriminative Vocabulary Learning for High-Level Person Representation[C],2016.

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