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Video Clarity: High Speed Data Mining for Video

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Video Clarity: High Speed Data Mining for Video. / Fisher, Pam; Alhabib, Abbas; Giotsas, Vasileios et al.
IBC 2015. London: Institution of Engineering and Technology, 2015.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Fisher, P, Alhabib, A, Giotsas, V & Andreopoulos, I 2015, Video Clarity: High Speed Data Mining for Video. in IBC 2015. Institution of Engineering and Technology, London. https://doi.org/10.1049/ibc.2015.0034

APA

Fisher, P., Alhabib, A., Giotsas, V., & Andreopoulos, I. (2015). Video Clarity: High Speed Data Mining for Video. In IBC 2015 Institution of Engineering and Technology. https://doi.org/10.1049/ibc.2015.0034

Vancouver

Fisher P, Alhabib A, Giotsas V, Andreopoulos I. Video Clarity: High Speed Data Mining for Video. In IBC 2015. London: Institution of Engineering and Technology. 2015 doi: 10.1049/ibc.2015.0034

Author

Fisher, Pam ; Alhabib, Abbas ; Giotsas, Vasileios et al. / Video Clarity : High Speed Data Mining for Video. IBC 2015. London : Institution of Engineering and Technology, 2015.

Bibtex

@inproceedings{9ebd4bf3f4e64195a8f3692c5c9abfbe,
title = "Video Clarity: High Speed Data Mining for Video",
abstract = "Video now dominates ICT networks and systems, representing over 64% of global IP traffic1 and over half of all storage within enterprises and data centers2. However, today video cannot be searched in the same way as alphanumeric data - this represents an unyielding `big data' problem. Current video search relies on resource-intensive human annotations placed in a database, as alphanumeric data. This paper describes a new technology innovated by BAFTA (British Academy of Film and Television Arts) and UCL (University College London), which addresses this issue. The technology extracts a compact video signature representing significant features of the video for search, which can then be used for a plethora of applications such as similarity detection, de-duplication of files, piracy detection, and semantic classification. The video signatures are extremely rich yet highly compact, sized at approximately 5 megabytes per running hour of video. This enables video to be searched at the speed of data, allowing video to become a firstclass citizen of ICT networks and systems.",
keywords = "high speed data mining, UCL, British Academy of Film and Television Arts, similarity detection, big data problem, file deduplication, University College London, video search, semantic classification, piracy detection, ICT systems, global IP traffic, video clarity, BAFTA, alphanumeric data, resource-intensive human annotations, data centers, video signature, ICT networks",
author = "Pam Fisher and Abbas Alhabib and Vasileios Giotsas and I. Andreopoulos",
year = "2015",
doi = "10.1049/ibc.2015.0034",
language = "English",
booktitle = "IBC 2015",
publisher = "Institution of Engineering and Technology",
address = "United Kingdom",

}

RIS

TY - GEN

T1 - Video Clarity

T2 - High Speed Data Mining for Video

AU - Fisher, Pam

AU - Alhabib, Abbas

AU - Giotsas, Vasileios

AU - Andreopoulos, I.

PY - 2015

Y1 - 2015

N2 - Video now dominates ICT networks and systems, representing over 64% of global IP traffic1 and over half of all storage within enterprises and data centers2. However, today video cannot be searched in the same way as alphanumeric data - this represents an unyielding `big data' problem. Current video search relies on resource-intensive human annotations placed in a database, as alphanumeric data. This paper describes a new technology innovated by BAFTA (British Academy of Film and Television Arts) and UCL (University College London), which addresses this issue. The technology extracts a compact video signature representing significant features of the video for search, which can then be used for a plethora of applications such as similarity detection, de-duplication of files, piracy detection, and semantic classification. The video signatures are extremely rich yet highly compact, sized at approximately 5 megabytes per running hour of video. This enables video to be searched at the speed of data, allowing video to become a firstclass citizen of ICT networks and systems.

AB - Video now dominates ICT networks and systems, representing over 64% of global IP traffic1 and over half of all storage within enterprises and data centers2. However, today video cannot be searched in the same way as alphanumeric data - this represents an unyielding `big data' problem. Current video search relies on resource-intensive human annotations placed in a database, as alphanumeric data. This paper describes a new technology innovated by BAFTA (British Academy of Film and Television Arts) and UCL (University College London), which addresses this issue. The technology extracts a compact video signature representing significant features of the video for search, which can then be used for a plethora of applications such as similarity detection, de-duplication of files, piracy detection, and semantic classification. The video signatures are extremely rich yet highly compact, sized at approximately 5 megabytes per running hour of video. This enables video to be searched at the speed of data, allowing video to become a firstclass citizen of ICT networks and systems.

KW - high speed data mining

KW - UCL

KW - British Academy of Film and Television Arts

KW - similarity detection

KW - big data problem

KW - file deduplication

KW - University College London

KW - video search

KW - semantic classification

KW - piracy detection

KW - ICT systems

KW - global IP traffic

KW - video clarity

KW - BAFTA

KW - alphanumeric data

KW - resource-intensive human annotations

KW - data centers

KW - video signature

KW - ICT networks

U2 - 10.1049/ibc.2015.0034

DO - 10.1049/ibc.2015.0034

M3 - Conference contribution/Paper

BT - IBC 2015

PB - Institution of Engineering and Technology

CY - London

ER -