Sparse representation over learned dictionary for symbol recognition

doi:10.1016/j.sigpro.2015.12.020

Sparse representation over learned dictionary for symbol recognition

2016-08-01

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Oriol Ramos Terrades *

DOI：10.1016/j.sigpro.2015.12.020

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Abstract

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In this paper we propose an original sparse vector model for symbol retrieval task. More specifically, 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. (C) 2016 Elsevier B.V. All rights reserved.

Keywords:

Symbol recognition

Sparse representation

Learned dictionary

Shape context

Interest points

Sparse representation over learned dictionary for symbol recognition

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