Flood Event Image Recognition via Social Media Image and Text Analysis

Min Jing, Bryan Scotney, SA Coleman, T.Martin McGinnity, Stephen Kelly, Xiubo Zhang, Khurshid Ahmad, Antje Schlaf, Sabine Grunder-Fahrer, Gerhard Heyer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The emergence of social media has led to a new era of information communication, in which vast amounts of information are available that is potentially valuable for emergency management. This supplements and enhances the data available through government bodies, emergency response agencies, and broadcasters. Techniques developed for visual content analysis can be useful tools to improve current emergency management systems. We present a new flood event scene recognition system based on social media visual content and text analysis. The concept of ontology is introduced that enables the text and image analysis to be linked at an atomic or hierarchical level. We accelerate web image analysis by using a new framework that incorporates a novel “Squiral” (square spiral) Image Processing addressing scheme with the state-of-art “Speeded-up Robust Features”. The focus of recognition was to identify the water or person images from the background images. Image URLs were obtained based on text analysis using English and German languages. We demonstrate the efficiency of the new image features and accuracy of recognition of flood water and persons within images, and hence the potential to enhance emergency management systems. The system for the atomic level recognition was evaluated using flood event related image data available from the US Federal Emergency Management Agency media library and public German Facebook pages and groups related to flood and flood aid. This evaluation was performed for and on behalf of an EU-FP7 Project Security Systems for Language and Image Analysis (Slandail), a system for managing disasters specifically with the help of digital media including social and legacy media. The system is intended to be incorporated by the project technology partners CID GmBH and DataPiano SA.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages6
Publication statusPublished - 24 Mar 2016
EventCOGNITIVE 2016 : The Eighth International Conference on Advanced Cognitive Technologies and Applications - Rome, Italy
Duration: 24 Mar 2016 → …

Conference

ConferenceCOGNITIVE 2016 : The Eighth International Conference on Advanced Cognitive Technologies and Applications
Period24/03/16 → …

Fingerprint

Image recognition
Image analysis
Digital storage
Security systems
Disasters
Ontology
Websites
Water
Image processing
Communication

Keywords

  • flood event recognition
  • fast image processing
  • social media analysis
  • multimodal data fusion
  • emergency management

Cite this

Jing, Min ; Scotney, Bryan ; Coleman, SA ; McGinnity, T.Martin ; Kelly, Stephen ; Zhang, Xiubo ; Ahmad, Khurshid ; Schlaf, Antje ; Grunder-Fahrer, Sabine ; Heyer, Gerhard. / Flood Event Image Recognition via Social Media Image and Text Analysis. Unknown Host Publication. 2016.
@inproceedings{770e4abd0af44973be3657e2b84f4dc0,
title = "Flood Event Image Recognition via Social Media Image and Text Analysis",
abstract = "The emergence of social media has led to a new era of information communication, in which vast amounts of information are available that is potentially valuable for emergency management. This supplements and enhances the data available through government bodies, emergency response agencies, and broadcasters. Techniques developed for visual content analysis can be useful tools to improve current emergency management systems. We present a new flood event scene recognition system based on social media visual content and text analysis. The concept of ontology is introduced that enables the text and image analysis to be linked at an atomic or hierarchical level. We accelerate web image analysis by using a new framework that incorporates a novel “Squiral” (square spiral) Image Processing addressing scheme with the state-of-art “Speeded-up Robust Features”. The focus of recognition was to identify the water or person images from the background images. Image URLs were obtained based on text analysis using English and German languages. We demonstrate the efficiency of the new image features and accuracy of recognition of flood water and persons within images, and hence the potential to enhance emergency management systems. The system for the atomic level recognition was evaluated using flood event related image data available from the US Federal Emergency Management Agency media library and public German Facebook pages and groups related to flood and flood aid. This evaluation was performed for and on behalf of an EU-FP7 Project Security Systems for Language and Image Analysis (Slandail), a system for managing disasters specifically with the help of digital media including social and legacy media. The system is intended to be incorporated by the project technology partners CID GmBH and DataPiano SA.",
keywords = "flood event recognition, fast image processing, social media analysis, multimodal data fusion, emergency management",
author = "Min Jing and Bryan Scotney and SA Coleman and T.Martin McGinnity and Stephen Kelly and Xiubo Zhang and Khurshid Ahmad and Antje Schlaf and Sabine Grunder-Fahrer and Gerhard Heyer",
year = "2016",
month = "3",
day = "24",
language = "English",
isbn = "978-1-61208-462-6",
booktitle = "Unknown Host Publication",

}

Jing, M, Scotney, B, Coleman, SA, McGinnity, TM, Kelly, S, Zhang, X, Ahmad, K, Schlaf, A, Grunder-Fahrer, S & Heyer, G 2016, Flood Event Image Recognition via Social Media Image and Text Analysis. in Unknown Host Publication. COGNITIVE 2016 : The Eighth International Conference on Advanced Cognitive Technologies and Applications, 24/03/16.

Flood Event Image Recognition via Social Media Image and Text Analysis. / Jing, Min; Scotney, Bryan; Coleman, SA; McGinnity, T.Martin; Kelly, Stephen; Zhang, Xiubo; Ahmad, Khurshid; Schlaf, Antje; Grunder-Fahrer, Sabine; Heyer, Gerhard.

Unknown Host Publication. 2016.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Flood Event Image Recognition via Social Media Image and Text Analysis

AU - Jing, Min

AU - Scotney, Bryan

AU - Coleman, SA

AU - McGinnity, T.Martin

AU - Kelly, Stephen

AU - Zhang, Xiubo

AU - Ahmad, Khurshid

AU - Schlaf, Antje

AU - Grunder-Fahrer, Sabine

AU - Heyer, Gerhard

PY - 2016/3/24

Y1 - 2016/3/24

N2 - The emergence of social media has led to a new era of information communication, in which vast amounts of information are available that is potentially valuable for emergency management. This supplements and enhances the data available through government bodies, emergency response agencies, and broadcasters. Techniques developed for visual content analysis can be useful tools to improve current emergency management systems. We present a new flood event scene recognition system based on social media visual content and text analysis. The concept of ontology is introduced that enables the text and image analysis to be linked at an atomic or hierarchical level. We accelerate web image analysis by using a new framework that incorporates a novel “Squiral” (square spiral) Image Processing addressing scheme with the state-of-art “Speeded-up Robust Features”. The focus of recognition was to identify the water or person images from the background images. Image URLs were obtained based on text analysis using English and German languages. We demonstrate the efficiency of the new image features and accuracy of recognition of flood water and persons within images, and hence the potential to enhance emergency management systems. The system for the atomic level recognition was evaluated using flood event related image data available from the US Federal Emergency Management Agency media library and public German Facebook pages and groups related to flood and flood aid. This evaluation was performed for and on behalf of an EU-FP7 Project Security Systems for Language and Image Analysis (Slandail), a system for managing disasters specifically with the help of digital media including social and legacy media. The system is intended to be incorporated by the project technology partners CID GmBH and DataPiano SA.

AB - The emergence of social media has led to a new era of information communication, in which vast amounts of information are available that is potentially valuable for emergency management. This supplements and enhances the data available through government bodies, emergency response agencies, and broadcasters. Techniques developed for visual content analysis can be useful tools to improve current emergency management systems. We present a new flood event scene recognition system based on social media visual content and text analysis. The concept of ontology is introduced that enables the text and image analysis to be linked at an atomic or hierarchical level. We accelerate web image analysis by using a new framework that incorporates a novel “Squiral” (square spiral) Image Processing addressing scheme with the state-of-art “Speeded-up Robust Features”. The focus of recognition was to identify the water or person images from the background images. Image URLs were obtained based on text analysis using English and German languages. We demonstrate the efficiency of the new image features and accuracy of recognition of flood water and persons within images, and hence the potential to enhance emergency management systems. The system for the atomic level recognition was evaluated using flood event related image data available from the US Federal Emergency Management Agency media library and public German Facebook pages and groups related to flood and flood aid. This evaluation was performed for and on behalf of an EU-FP7 Project Security Systems for Language and Image Analysis (Slandail), a system for managing disasters specifically with the help of digital media including social and legacy media. The system is intended to be incorporated by the project technology partners CID GmBH and DataPiano SA.

KW - flood event recognition

KW - fast image processing

KW - social media analysis

KW - multimodal data fusion

KW - emergency management

M3 - Conference contribution

SN - 978-1-61208-462-6

BT - Unknown Host Publication

ER -