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Matheus Oliveira Franca

dblp:249/8099 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 67% Geometric modeling and processing · 33%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing › shape analysis
3d model analysis		 0.412019
BUDA.ART: A Multimodal Content Based Analysis and Retrieval System for Buddha Statues · ACM Multimedia 2019
Multimedia analysis and retrieval › image retrieval
content-based image retrieval		 0.412019
BUDA.ART: A Multimodal Content Based Analysis and Retrieval System for Buddha Statues · ACM Multimedia 2019
Multimedia analysis and retrieval
cross-modal retrieval		 0.412019
BUDA.ART: A Multimodal Content Based Analysis and Retrieval System for Buddha Statues · ACM Multimedia 2019
Computational social science and digital humanities
art history		 0.112019
BUDA.ART: A Multimodal Content Based Analysis and Retrieval System for Buddha Statues · ACM Multimedia 2019

Methods — techniques the papers use, named apart from their topics

facial embedding · 0.8CBIR · 0.8
YearPublicationVenuePosition
2019 BUDA.ART: A Multimodal Content Based Analysis and Retrieval System for Buddha Statues
abstract
We introduce BUDA.ART, a system designed to assist researchers in Art History, to explore and analyze an archive of pictures of Buddha statues. The system combines different CBIR and classical retrieval techniques to assemble 2D pictures, 3D statue scans and meta-data, that is focused on the Buddha facial characteristics. We build the system from an archive of 50,000 Buddhism pictures, identify unique Buddha statues, extract contextual information, and provide specific facial embedding to first index the archive. The system allows for mobile, on-site search, and to explore similarities of statues in the archive. In addition, we provide search visualization and 3D analysis of the statues.
Benjamin Renoust, Matheus Oliveira Franca, Jacob Chan, Van Le, Ayaka Uesaka, Yuta Nakashima, Hajime Nagahara, Jueren Wang, Yutaka Fujioka
 ACM Multimedia2