Unsupervised local regressive attributes for pedestrian re-identification

Billy Peralta, Luis Caro, Alvaro Soto

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


Discovering of attributes is a challenging task in computer vision due to uncertainty about the attributes, which is caused mainly by the lack of semantic meaning in image parts. A usual scheme for facing attribute discovering is to divide the feature space using binary variables. Moreover, we can assume to know the attributes and by using expert information we can give a degree of attribute beyond only two values. Nonetheless, a binary variable could not be very informative, and we could not have access to expert information. In this work, we propose to discover linear regressive codes using image regions guided by a supervised criteria where the obtained codes obtain better generalization properties. We found that the discovered regressive codes can be successfully re-used in other visual datasets. As a future work, we plan to explore richer codification structures than lineal mapping considering efficient computation.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings
EditorsSergio Velastin, Marcelo Mendoza
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319751924
Publication statusPublished - 1 Jan 2018
Event22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017 - Valparaiso, Chile
Duration: 7 Nov 201710 Nov 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10657 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017


  • Attribute discovery
  • Pedestrian re-identification
  • Unsupervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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