Early active learning with pairwise constraint for person re-identification

Wenhe Liu, Xiaojun Chang, Ling Chen, Yi Yang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

8 Citations (Scopus)


Research on person re-identification (re-id) has attached much attention in the machine learning field in recent years. With sufficient labeled training data, supervised re-id algorithm can obtain promising performance. However, producing labeled data for training supervised re-id models is an extremely challenging and time-consuming task because it requires every pair of images across no-overlapping camera views to be labeled. Moreover, in the early stage of experiments, when labor resources are limited, only a small number of data can be labeled. Thus, it is essential to design an effective algorithm to select the most representative samples. This is referred as early active learning or early stage experimental design problem. The pairwise relationship plays a vital role in the re-id problem, but most of the existing early active learning algorithms fail to consider this relationship. To overcome this limitation, we propose a novel and efficient early active learning algorithm with a pairwise constraint for person re-identification in this paper. By introducing the pairwise constraint, the closeness of similar representations of instances is enforced in active learning. This benefits the performance of active learning for re-id. Extensive experimental results on four benchmark datasets confirm the superiority of the proposed algorithm.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2017 Skopje, Macedonia, September 18–22, 2017 Proceedings, Part I
EditorsMichelangelo Ceci, Jaakko Hollmen, Ljupco Todorovski, Celine Vens, Saso Dzeroski
Place of PublicationCham Switzerland
Number of pages16
ISBN (Electronic)9783319712499
ISBN (Print)9783319712482
Publication statusPublished - 2017
Externally publishedYes
EventEuropean Conference on Machine Learning European Conference on Principles and Practice of Knowledge Discovery in Databases 2017 - Skopje, North Macedonia
Duration: 18 Sep 201722 Sep 2017
Conference number: 15th
https://link.springer.com/book/10.1007/978-3-319-71249-9 (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Machine Learning European Conference on Principles and Practice of Knowledge Discovery in Databases 2017
Abbreviated titleECML PKDD 2017
Country/TerritoryNorth Macedonia
Internet address


  • Early active learning
  • Person re-identification

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