Unsupervised local regressive attributes for pedestrian re-identification

Billy Peralta, Luis Caro, Alvaro Soto

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings
EditoresSergio Velastin, Marcelo Mendoza
EditorialSpringer Verlag
Páginas517-524
Número de páginas8
ISBN (versión impresa)9783319751924
DOI
EstadoPublicada - 1 ene 2018
Evento22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017 - Valparaiso, Chile
Duración: 7 nov 201710 nov 2017

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10657 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017
PaísChile
CiudadValparaiso
Período7/11/1710/11/17

Áreas temáticas de ASJC Scopus

  • Ciencia computacional teórica
  • Informática (todo)

Huella Profundice en los temas de investigación de 'Unsupervised local regressive attributes for pedestrian re-identification'. En conjunto forman una huella única.

  • Citar esto

    Peralta, B., Caro, L., & Soto, A. (2018). Unsupervised local regressive attributes for pedestrian re-identification. En S. Velastin, & M. Mendoza (Eds.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings (pp. 517-524). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10657 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-75193-1_62