|Tiêu đề||Sparse Representation over Learned Dictionary for Symbol Recognition|
|Loại công bố||Journal Article|
|Năm xuất bản||2016|
|Tác giả||Do, T-H, Tabbone, S, Ramos-Terrades, O|
|Tạp chí||Signal Processing|
|Thời gian xuất bản||1/2016|
|Tóm tắt|| |
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.