InforLorV4, Main, Exploration, bibRecord, 000C91

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

Identifieur interne : 000C91 ( Main/Exploration ); précédent : 000C90; suivant : 000C92

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

Auteurs : Thanh Ha Do [France] ; Salvatore Tabbone [France] ; Oriol Ramos Terrades [Espagne]

Source :

RBID : Hal:hal-00939182

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.

Url:

https://hal.inria.fr/hal-00939182

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 003555
to stream Hal, to step Curation: 003555
to stream Hal, to step Checkpoint: 000C37
to stream Main, to step Merge: 000C99
to stream Main, to step Curation: 000C91

Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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<name sortKey="Tabbone, Salvatore" sort="Tabbone, Salvatore" uniqKey="Tabbone S" first="Salvatore" last="Tabbone">Salvatore Tabbone</name>
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<country name="Espagne"><noRegion><name sortKey="Ramos Terrades, Oriol" sort="Ramos Terrades, Oriol" uniqKey="Ramos Terrades O" first="Oriol" last="Ramos Terrades">Oriol Ramos Terrades</name>
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New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation

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

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Abstract

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