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. HighlightsWe study how to use sparse representations for symbol description in retrieval tasks.We propose an original extension of the tf-idf model to sparse representations.We use the K-SVD algorithm using the shape context descriptor applied on keypoints.This is the first attempt of using this kind of representation in symbol retrieval tasks.
['Thanh Ha Do', 'Salvatore Tabbone', 'Oriol Ramos Terrades']
Sparse representation over learned dictionary for symbol recognition