@Article{ThanhHaDo2016,
author="Thanh Ha Do
and Salvatore Tabbone
and Oriol Ramos Terrades",
title="Sparse representation over learned dictionary for symbol recognition",
journal="Signal Processing",
year="2016",
volume="125",
pages="36--47",
optkeywords="Symbol Recognition",
optkeywords="Sparse Representation",
optkeywords="Learned Dictionary",
optkeywords="Shape Context",
optkeywords="Interest Points",
abstract="In this paper we propose an original sparse vector model for symbol retrieval task. More specically, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols.",
optnote="DAG; 600.061; 600.077",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2946), last updated on Thu, 07 Mar 2019 11:04:29 +0100",
opturl="https://doi.org/10.1016/j.sigpro.2015.12.020",
file=":http://refbase.cvc.uab.es/files/DTR2016.pdf:PDF"
}