On deeply learning features for automatic person re-identification - Couverture souple

Franco, Alexandre; Oliveira, Luciano

 
9783330029101: On deeply learning features for automatic person re-identification
Couverture souple
ISBN 10 : 3330029102 ISBN 13 : 9783330029101
Editeur : LAP LAMBERT Academic Publishing, 2017

Synopsis

The automatic person re-identification problem resides in matching an unknown person image to a database of previously labeled images of people. Comparison among two image features is commonly accomplished by distance metrics. Although features and distance metrics can be handcrafted or trainable, the latter type has demonstrated more potential to breakthroughs in achieving state-of-the-art performance over public data sets. A recent paradigm that allows to work with trainable features is deep learning. In this book, we present a novel deep learning strategy, so called coarse-to-fine learning (CFL), as well as a novel type of feature - the convolutional covariance features (CCF), for person re-identification. CFL is based on the human learning process. After extracting the convolutional features via CFL, those ones are then wrapped in covariance matrices, composing the CCF. The performance of the proposed framework was assessed comparatively against 18 state-of-the-art methods by using public data sets (VIPeR, i-LIDS, CUHK01 and CUHK03), achieving superior performance.

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Présentation de l'éditeur

The automatic person re-identification problem resides in matching an unknown person image to a database of previously labeled images of people. Comparison among two image features is commonly accomplished by distance metrics. Although features and distance metrics can be handcrafted or trainable, the latter type has demonstrated more potential to breakthroughs in achieving state-of-the-art performance over public data sets. A recent paradigm that allows to work with trainable features is deep learning. In this book, we present a novel deep learning strategy, so called coarse-to-fine learning (CFL), as well as a novel type of feature - the convolutional covariance features (CCF), for person re-identification. CFL is based on the human learning process. After extracting the convolutional features via CFL, those ones are then wrapped in covariance matrices, composing the CCF. The performance of the proposed framework was assessed comparatively against 18 state-of-the-art methods by using public data sets (VIPeR, i-LIDS, CUHK01 and CUHK03), achieving superior performance.

Biographie de l'auteur

Alexandre Franco received his PhD in Mechatronics from Federal University of Bahia, Brazil, in 2016. His main research area is image pattern recognition. He has published several papers in the field of computer vision and pattern recognition.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2017
Langue
anglais
ISBN 10 
3330029102
ISBN 13 
9783330029101
Reliure
Broché
Nombre de pages
112
Coordonnées du fabricant
non disponible
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