TY  - JOUR
AU  - Thanh Ha Do
AU  - Salvatore Tabbone
AU  - Oriol Ramos Terrades
PY  - 2016//
TI  - Sparse representation over learned dictionary for symbol recognition
T2  - SP
JO  - Signal Processing
SP  - 36
EP  - 47
VL  - 125
KW  - Symbol Recognition
KW  - Sparse Representation
KW  - Learned Dictionary
KW  - Shape Context
KW  - Interest Points
N2  - 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.
UR  - https://doi.org/10.1016/j.sigpro.2015.12.020
L1  - http://refbase.cvc.uab.es/files/DTR2016.pdf
N1  - DAG; 600.061; 600.077
ID  - Thanh Ha Do2016
ER  -