Heritage Iconographic Content Structuring: from Automatic Linking to Visual Validation

Author:

Blettery Emile1ORCID,Gouet-Brunet ValéRie2ORCID

Affiliation:

1. LASTIG, Université Gustave Eiffel, ENSG, IGN, France and Ville de Paris, DAC, DHAAP, France

2. LASTIG, Université Gustave Eiffel, ENSG, IGN, France

Abstract

This article presents a global framework dedicated to the structuring of iconographic heritage collections. To alleviate the poor interlinking both between collections and contents, a first step of automatic linking exploiting content-based image retrieval approaches is evaluated and adapted to the visual variability of such heritage contents. To ensure understanding and analysis of the contents in a structured fashion, a 3D immersive web platform is also introduced alongside visual-based analysis tools. Finally, by exploiting both automatic linking and manual interventions in the visualization platform, an iterative, semi-automatic structuring pipeline is proposed to solve difficult cases missed by automatic structuring, and then improve structuring optimally. Here, we demonstrate the potential of the proposal on the geographic iconographic heritage of Paris, with a dataset of 10k images belonging to several institutions, thus poorly connected nor organized globally.

Publisher

Association for Computing Machinery (ACM)

Reference71 articles.

1. Albert Kahn museum . 2016. Albert Kahn Museum’s Collections Browsing Platform. Retrieved from https://opendata.hauts-de-seine.fr/explore/dataset/archives-de-la-planete/information/?disjunctive.operateur

2. Validity of the single processor approach to achieving large scale computing capabilities

3. Aggregating Local Deep Features for Image Retrieval

4. Re-Ranking via Metric Fusion for Object Retrieval and Person Re-Identification

5. Daniel Barath, Jiri Matas, and Jana Noskova. 2019. MAGSAC: Marginalizing sample consensus. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10197–10205.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3