Aggregating Local Descriptors for Epigraphs Recognition

In this paper, we consider the task of recognizing epigraphs in images such as photos taken using mobile devices. Given a set of 17,155 photos related to 14,560 epigraphs, we used a k-NearestNeighbor approach in order to perform the recognition. The contribution of this work is in evaluating state-o...

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Main Authors: Giuseppe Amato, Fabrizio Falchi, Fausto Rabitti, Lucia Vadicamo
Format: Article
Language:English
Published: Bulgarian Academy of Sciences, Institute of Mathematics and Informatics 2014-09-01
Series:Digital Presentation and Preservation of Cultural and Scientific Heritage
Subjects:
Online Access:https://dipp.math.bas.bg/dipp/article/view/347
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author Giuseppe Amato
Fabrizio Falchi
Fausto Rabitti
Lucia Vadicamo
author_facet Giuseppe Amato
Fabrizio Falchi
Fausto Rabitti
Lucia Vadicamo
author_sort Giuseppe Amato
collection DOAJ
description In this paper, we consider the task of recognizing epigraphs in images such as photos taken using mobile devices. Given a set of 17,155 photos related to 14,560 epigraphs, we used a k-NearestNeighbor approach in order to perform the recognition. The contribution of this work is in evaluating state-ofthe-art visual object recognition techniques in this specific context. The experimental results conducted show that Vector of Locally Aggregated Descriptors obtained aggregating SIFT descriptors is the best choice for this task.
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spelling doaj.art-0df3b2edbe6244e1a371ede15fa2cd4b2022-12-22T02:42:50ZengBulgarian Academy of Sciences, Institute of Mathematics and InformaticsDigital Presentation and Preservation of Cultural and Scientific Heritage1314-40062535-03662014-09-01410.55630/dipp.2014.4.6Aggregating Local Descriptors for Epigraphs RecognitionGiuseppe Amato0Fabrizio Falchi1Fausto Rabitti2Lucia Vadicamo3ISTI-CNR, via G. Moruzzi, 1 – 56124 Pisa (Italy)ISTI-CNR, via G. Moruzzi, 1 – 56124 Pisa (Italy)ISTI-CNR, via G. Moruzzi, 1 – 56124 Pisa (Italy)ISTI-CNR, via G. Moruzzi, 1 – 56124 Pisa (Italy)In this paper, we consider the task of recognizing epigraphs in images such as photos taken using mobile devices. Given a set of 17,155 photos related to 14,560 epigraphs, we used a k-NearestNeighbor approach in order to perform the recognition. The contribution of this work is in evaluating state-ofthe-art visual object recognition techniques in this specific context. The experimental results conducted show that Vector of Locally Aggregated Descriptors obtained aggregating SIFT descriptors is the best choice for this task.https://dipp.math.bas.bg/dipp/article/view/347Epigraphs RecognitionObject RecognitionContent-Base Image RetrievalBag-of-FeaturesVLAD
spellingShingle Giuseppe Amato
Fabrizio Falchi
Fausto Rabitti
Lucia Vadicamo
Aggregating Local Descriptors for Epigraphs Recognition
Digital Presentation and Preservation of Cultural and Scientific Heritage
Epigraphs Recognition
Object Recognition
Content-Base Image Retrieval
Bag-of-Features
VLAD
title Aggregating Local Descriptors for Epigraphs Recognition
title_full Aggregating Local Descriptors for Epigraphs Recognition
title_fullStr Aggregating Local Descriptors for Epigraphs Recognition
title_full_unstemmed Aggregating Local Descriptors for Epigraphs Recognition
title_short Aggregating Local Descriptors for Epigraphs Recognition
title_sort aggregating local descriptors for epigraphs recognition
topic Epigraphs Recognition
Object Recognition
Content-Base Image Retrieval
Bag-of-Features
VLAD
url https://dipp.math.bas.bg/dipp/article/view/347
work_keys_str_mv AT giuseppeamato aggregatinglocaldescriptorsforepigraphsrecognition
AT fabriziofalchi aggregatinglocaldescriptorsforepigraphsrecognition
AT faustorabitti aggregatinglocaldescriptorsforepigraphsrecognition
AT luciavadicamo aggregatinglocaldescriptorsforepigraphsrecognition