New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation

Thanh Ha Do 1 Salvatore Tabbone 1 Oriol Ramos Terrades 2, *
* Auteur correspondant
1 QGAR - Querying Graphics through Analysis and Recognition
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation in a visual vocabulary whose visual words are columns in the learned dictionary. The retrieval task is performed by ranking symbols based on similarity between vector models. The evaluation of our method, using benchmark datasets, demonstrates the validity of our approach and shows that it outperforms related state-of-theart methods.
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Communication dans un congrès
IEEE, 2013
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Thanh Ha Do, Salvatore Tabbone, Oriol Ramos Terrades. New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation. IEEE, 2013. 〈hal-00939182〉

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