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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
Conference NameAssociation for the Advancement of Artificial Intelligence
Source PublicationAAAI
Conference Date2016, 02.12-02.17
Conference PlacePhoenix, USA
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
KeywordPerson Re-identification Metric Embedded Discriminative Vocabulary Learning High-level Person Representation
Document Type会议论文
Corresponding AuthorYang Yang
Recommended Citation
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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