TY - GEN
T1 - Unsupervised local regressive attributes for pedestrian re-identification
AU - Peralta, Billy
AU - Caro, Luis
AU - Soto, Alvaro
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Attribute discovery
KW - Pedestrian re-identification
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85042213112&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-75193-1_62
DO - 10.1007/978-3-319-75193-1_62
M3 - Conference contribution
AN - SCOPUS:85042213112
SN - 9783319751924
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 517
EP - 524
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings
A2 - Velastin, Sergio
A2 - Mendoza, Marcelo
PB - Springer Verlag
T2 - 22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017
Y2 - 7 November 2017 through 10 November 2017
ER -