research-article

Automated social event detection in large photo collections

Authors:

Maia Zaharieva,

Matthias Zeppelzauer,

Christian BreitenederAuthors Info & Claims

ICMR '13: Proceedings of the 3rd ACM conference on International conference on multimedia retrieval

Pages 167 - 174

https://doi.org/10.1145/2461466.2461495

Published: 16 April 2013 Publication History

Abstract

The detection of a specific social event requires for high semantic understanding in the interpretation of particular event characteristics such as its type and location. In many cases, photos capturing different events at the same (or highly similar) locations can hardly be distinguished by each other. Available metadata can provide assistance where there is no expert knowledge at hand. However, metadata often lack completeness and reliability. In this paper, we explore the feasibility of a fully automated approach for the detection of specific social events. In comparison to related approaches, we do not incorporate query-specific processing and we perform no manual adaptation of the input query. The resulting approach is applicable to arbitrary event types.

References

[1]

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool. Speeded-up robust features (surf). Journal of Computer Vision and Image Understanding, 110(3):346--359, 2008.

Digital Library

[2]

A. Bosch, A. Zisserman, and X. Munoz. Image classification using random forests and ferns. In IEEE International Conference on Computer Vision (ICCV'07), pages 1--8, 2007.

[3]

M. Brenner and E. Izquierdo. Mediaeval benchmark social event detection in collaborative photo collections. In MediaEval 2011 Workshop, 2011.

[4]

M. Brenner and E. Izquierdo. Qmul @ mediaeval 2012: Social event detection in collaborative photo collections. In MediaEval 2012 Workshop, 2012.

[5]

M. Brenner and E. Izquierdo. Social event detection and retrieval in collaborative photo collections. In ACM International Conference on Multimedia Retrieval (ICMR'12), pages 21:1--21:8, 2012.

Digital Library

[6]

D. C. Bulterman. Is it time for a moratorium on metadata? IEEE MultiMedia, 11(4):10--17, 2004.

Digital Library

[7]

L. Chen and A. Roy. Event detection from flickr data through wavelet-based spatial analysis. In ACM Conference on Information and Knowledge Management (CIKM'09), pages 523--532, 2009.

Digital Library

[8]

M.-S. Dao, G. Boato, F. G. D. Natale, and T.-V. T. NGuyen. A watershed-based social events detection method with support of external data sources. In MediaEval 2012 Workshop, 2012.

[9]

M. Dubinko, R. Kumar, J. Magnani, J. Novak, P. Raghavan, and A. Tomkins. Visualizing tags over time. ACM Transactions on the Web, 1(2), 2007.

Digital Library

[10]

J. Foote and M. Cooper. Media segmentation using self-similarity decomposition. In Proc. SPIE 5021 Storage and Retrieval for Media Databases, pages 167--175, 2003.

[11]

T. Hintsa and S. V. M. Melin. Leveraging linked data in social event detection. In MediaEval 2011 Workshop, 2011.

[12]

X. Liu, B. Huet, and R. Troncy. Eurecom @ mediaeval 2011 social event detection task. In MediaEval 2011 Workshop, 2011.

[13]

D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91--110, Nov. 2004.

Digital Library

[14]

M. Morchid and G. Linares. Mediaeval benchmark: Social event detection using lda and external resources. In MediaEval 2011 Workshop, 2011.

[15]

S. Papadopoulos, Y. Kompatsiaris, and A. Vakali. Certh @ mediaeval 2011 social event detection task. In MediaEval 2011 Workshop, 2011.

[16]

S. Papadopoulos, E. Schinas, V. Mezaris, R. Troncy, and I. Kompatsiaris. Social event detection at mediaeval 2012: Challenges, dataset and evaluation. In MediaEval 2012 Workshop, 2012.

[17]

S. Papadopoulos, C. Zigkolis, Y. Kompatsiaris, and A. Vakali. Cluster-based landmark and event detection for tagged photo collections. IEEE MultiMedia, 18(1):52--63, 2011.

Digital Library

[18]

G. Petkos, S. Papadopoulos, and Y. Kompatsiaris. Social event detection using multimodal clustering and integrating supervisory signals. In ACM International Conference on Multimedia Retrieval (ICMR'12), pages 23:1--23:8, 2012.

Digital Library

[19]

T. Rattenbury, N. Good, and M. Naaman. Towards automatic extraction of event and place semantics from flickr tags. In ACM SIGIR International Conference on Research and Development in Information Retrieval, pages 103--110, 2007.

Digital Library

[20]

T. Reuter and P. Cimiano. Event-based classification of social media streams. In ACM International Conference on Multimedia Retrieval (ICMR'12), pages 22:1--22:8, 2012.

Digital Library

[21]

M. Ruocco and H. Ramampiaro. Ntnu@mediaeval 2011 social event detection task (sed). In MediaEval 2011 Workshop, 2011.

[22]

E. Schinas, G. Petkos, S. Papadopoulos, and Y. Kompatsiaris. Certh @ mediaeval 2012 social event detection task. In MediaEval 2012 Workshop, 2012.

[23]

K. N. Vavliakis, F. A. Tzima, and P. A. Mitkas. Event detection via lda for the mediaeval2012 sed task. In MediaEval 2012 Workshop, 2012.

[24]

Y. Wang, L. Xie, and H. Sunduram. Social event detection with clustering and filtering. In MediaEval 2011 Workshop, 2011.

[25]

M. Zeppelzauer, M. Zaharieva, and C. Breiteneder. A generic approach for social event detection in large photo collections. In MediaEval 2012 Workshop, 2012.

Cited By

Kovalchuk MFilatova AKorneev AKoreneva MNasonov DVoskresenskii ABoukhanovsky A(2024)SemConvTree: Semantic Convolutional Quadtrees for Multi-Scale Event Detection in Smart CitySmart Cities10.3390/smartcities70501077:5(2763-2780)Online publication date: 28-Sep-2024
https://doi.org/10.3390/smartcities7050107
Lin ZXie JYang ZYu YLi QCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Generalized News Event Discovery via Dynamic Augmentation and Entropy OptimizationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681157(10018-10026)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681157
Singh TKumari MGupta DSiniak N(2023)Axiomatic Analysis of Pre‐Processing Methodologies Using Machine Learning in Text MiningConvergence of Cloud with AI for Big Data Analytics10.1002/9781119905233.ch11(229-256)Online publication date: 10-Feb-2023
https://doi.org/10.1002/9781119905233.ch11
Show More Cited By

Index Terms

Automated social event detection in large photo collections
1. Information systems
  1. Information retrieval

Recommendations

Social Event Mining in Large Photo Collections
ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

A significant part of publicly available photos on the Internet depicts a variety of different social events. In order to organize this steadily growing media content and to make it easily accessible, novel indexing methods are required. Essential ...
Social event detection and retrieval in collaborative photo collections
ICMR '12: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval

In this paper, we present an approach to detect social events and retrieve associated photos in collaboratively annotated photo collections. We combine data of various modalities such as time, location, and textual and visual features within a framework ...
Improving Event Detection by Automatically Assessing Validity of Event Occurrence in Text
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Manually inspecting text to assess whether an event occurs in a document collection is an onerous and time consuming task. Although a manual inspection to discard the false events would increase the precision of automatically detected sets of events, it ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICMR '13: Proceedings of the 3rd ACM conference on International conference on multimedia retrieval

April 2013

362 pages

ISBN:9781450320337

DOI:10.1145/2461466

General Chairs:
Ramesh Jain
University of California, Irvine, USA
,
Balakrisknan Prabhakaran
University of Texas at Dallas, USA
,
Program Chairs:
Marcel Worring
University of Amsterdam, The Netherlands
,
John Smith
IBM Research, New York, USA
,
Tat-Seng Chua
National University of Singapore

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICMR'13

Sponsor:

SIGMM

ICMR'13: International Conference on Multimedia Retrieval

April 16 - 20, 2013

Texas, Dallas, USA

Acceptance Rates

ICMR '13 Paper Acceptance Rate 38 of 96 submissions, 40%;

Overall Acceptance Rate 254 of 830 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

30
Total Citations
View Citations
315
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kovalchuk MFilatova AKorneev AKoreneva MNasonov DVoskresenskii ABoukhanovsky A(2024)SemConvTree: Semantic Convolutional Quadtrees for Multi-Scale Event Detection in Smart CitySmart Cities10.3390/smartcities70501077:5(2763-2780)Online publication date: 28-Sep-2024
https://doi.org/10.3390/smartcities7050107
Lin ZXie JYang ZYu YLi QCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Generalized News Event Discovery via Dynamic Augmentation and Entropy OptimizationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681157(10018-10026)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681157
Singh TKumari MGupta DSiniak N(2023)Axiomatic Analysis of Pre‐Processing Methodologies Using Machine Learning in Text MiningConvergence of Cloud with AI for Big Data Analytics10.1002/9781119905233.ch11(229-256)Online publication date: 10-Feb-2023
https://doi.org/10.1002/9781119905233.ch11
Xiao KQian ZQin B(2022)A Survey of Data Representation for Multi-Modality Event Detection and EvolutionApplied Sciences10.3390/app1204220412:4(2204)Online publication date: 20-Feb-2022
https://doi.org/10.3390/app12042204
Afyouni IAghbari ZRazack R(2022)Multi-feature, multi-modal, and multi-source social event detectionInformation Fusion10.1016/j.inffus.2021.10.01379:C(279-308)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.inffus.2021.10.013
Cheung MSun WZhou J(2021)Discovering Social Connections using Event ImagesProceedings of the 3rd ACM International Conference on Multimedia in Asia10.1145/3469877.3493699(1-5)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1145/3469877.3493699
Afyouni IKhan AAghbari ZDesai BCho W(2020)Spatio-temporal event discovery in the big social data eraProceedings of the 24th Symposium on International Database Engineering & Applications10.1145/3410566.3410568(1-6)Online publication date: 12-Aug-2020
https://dl.acm.org/doi/10.1145/3410566.3410568
Shah RMahata DChoudhary VBajpai R(2018)Multimodal Semantics and Affective Computing from Multimedia ContentIntelligent Multidimensional Data and Image Processing10.4018/978-1-5225-5246-8.ch014(359-382)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5246-8.ch014
Lin ZYang ZSitu RHuang FLv JLi QLiu W(2018)Improving Maximum Classifier Discrepancy by Considering Joint Distribution for Domain AdaptationWeb Information Systems Engineering – WISE 201810.1007/978-3-030-02925-8_18(253-268)Online publication date: 21-Oct-2018
https://doi.org/10.1007/978-3-030-02925-8_18
Shah RZimmermann RShah RZimmermann R(2017)Conclusion and Future WorkMultimodal Analysis of User-Generated Multimedia Content10.1007/978-3-319-61807-4_8(235-260)Online publication date: 1-Sep-2017
https://doi.org/10.1007/978-3-319-61807-4_8
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents