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Person Re-identification by Bidirectional Projection

Published: 10 July 2014 Publication History

Abstract

Person re-identification plays an important role in video surveillance system. It can be regarded as an image retrieval process which aims to find the same person in multi-camera networks. Many existing methods learn a pairwise similarity measure by mapping the raw feature to a latent subspace to make the data more discriminative. However, most of these methods project all the data into the same subspace ignoring the different characteristics that the same person and different person hold. To solve the aforementioned problem, a pairwise based method is proposed by projecting the raw feature onto two discriminative subspaces according to whether a image pair is of the same class. The proposed method constructs a relative and pairwise model by using the logistic loss function to give a soft measure of the pairwise loss. Meanwhile, a trace norm regularization is used to create the convexity of the objective function, which also help to limit the dimension of the subspaces. Experiments carried on the benchmark dataset VIPeR show that the proposed model obtains better results compared with state-of-the-art methods.

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cover image ACM Other conferences
ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service
July 2014
430 pages
ISBN:9781450328104
DOI:10.1145/2632856
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • NSF of China: National Natural Science Foundation of China
  • Beijing ACM SIGMM Chapter

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2014

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Author Tags

  1. Bidirectional projection
  2. Person re-identification
  3. Video surveillance

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ICIMCS '14

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Overall Acceptance Rate 163 of 456 submissions, 36%

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