research-article

Open-source column: content-based multimedia retrieval using vitrivr

Authors:

Luca Rossetto,

Ivan Giangreco,

Ralph Gasser,

Heiko SchuldtAuthors Info & Claims

ACM SIGMultimedia Records, Volume 9, Issue 3

Article No.: 8, Page 8

https://doi.org/10.1145/3178422.3178430

Published: 09 January 2018 Publication History

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Abstract

URL: https://vitrivr.org/

vitrivr is an open source retrieval system capable of processing multimedia documents such as images, videos, music, and 3D-models. It supports a wealth of content based features for multiple modalities and comes with a ready-to-use Docker image and user interface. We focus on the vitrivr stack in its second version, comprised of ADAMPro 2.0.0, Cineast 2.0.0, and the user interface Vitrivr NG 1.0.0. All components of vitrivr are available under an MIT license.

References

[1]

Rossetto, L.; Giangreco, I.; Tanase, C.; Schuldt, H. . vitrivr: A Flexible Retrieval Stack Supporting Multiple Query Modes for Searching in Multimedia Collections. In: ACM International Conference on Multimedia (ACM MM), 2016

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[2]

Giangreco, I.; Schuldt, H. . ADAMpro: Database Support for Big Multimedia Retrieval. In: Datenbank-Spektrum, 16(1):17--26, 2016.

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[3]

Rossetto, L.; Giangreco, I.; Schuldt, H. Cineast: a Multi-Feature Sketch-Based Video Retrieval Engine. In: IEEE International Symposium on Multimedia (ISM), 2014

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Cited By

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Gasser RRossetto LHeller SSchuldt H(2022)Multimedia Retrieval and Analysis with Cottontail DBACM SIGMultimedia Records10.1145/3577934.357794013:1(1-1)Online publication date: 19-Dec-2022
https://dl.acm.org/doi/10.1145/3577934.3577940
Rossetto LGasser RLokoc JBailer WSchoeffmann KMuenzer BSoucek TNguyen PBolettieri PLeibetseder AVrochidis S(2021)Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019IEEE Transactions on Multimedia10.1109/TMM.2020.298094423(243-256)Online publication date: 2021
https://doi.org/10.1109/TMM.2020.2980944
Spolaôr NLee HTakaki WEnsina LParmezan AOliva JCoy CWu F(2021)A video indexing and retrieval computational prototype based on transcribed speechMultimedia Tools and Applications10.1007/s11042-021-11401-1Online publication date: 30-Aug-2021
https://doi.org/10.1007/s11042-021-11401-1

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Information

Published In

ACM SIGMultimedia Records Volume 9, Issue 3

December 2017

9 pages

EISSN:1947-4598

DOI:10.1145/3178422

Editors:
Pablo Cesar
Centrum Wiskunde & Informatica (CWI), Netherlands
,
Mario Montagud,
Carsten Griwodz

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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 January 2018

Published in SIGMM Volume 9, Issue 3

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Gasser RRossetto LHeller SSchuldt H(2022)Multimedia Retrieval and Analysis with Cottontail DBACM SIGMultimedia Records10.1145/3577934.357794013:1(1-1)Online publication date: 19-Dec-2022
https://dl.acm.org/doi/10.1145/3577934.3577940
Rossetto LGasser RLokoc JBailer WSchoeffmann KMuenzer BSoucek TNguyen PBolettieri PLeibetseder AVrochidis S(2021)Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019IEEE Transactions on Multimedia10.1109/TMM.2020.298094423(243-256)Online publication date: 2021
https://doi.org/10.1109/TMM.2020.2980944
Spolaôr NLee HTakaki WEnsina LParmezan AOliva JCoy CWu F(2021)A video indexing and retrieval computational prototype based on transcribed speechMultimedia Tools and Applications10.1007/s11042-021-11401-1Online publication date: 30-Aug-2021
https://doi.org/10.1007/s11042-021-11401-1

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