In this paper, we propose a framework for recording, analysing, indexing and retrieving news videos such as the BBC one o'clock news. We believe that such a framework will be useful to identify implicit indicators of relevance, a nearly untouched area in adaptive multimedia retrieval. Due to its advantages as a Web application and its up-to-date content, it can be a promising approach to motivate a broad quantity of users to interact with the system

Hopfgartner, F.

Jose, J.M.

English

Enlighten: Publications

  
 
 
 
 
 
Hopfgartner, F. and Jose, J.M. (2007) A news video retrieval framework 
for the study of implicit relevance. In: Second International Workshop on 
Semantic Media Adaptation and Personalization, 17-18 Dec 2007, 
London, UK. 
 
 
http://eprints.gla.ac.uk/5752/ 
 
Deposited on: 25 May 2009 
 
 
Enlighten – Research publications by members of the University of Glasgow 
http://eprints.gla.ac.uk 
A News Video Retrieval Framework for the Study of Implicit Relevance
Feedback
Frank Hopfgartner
Department of Computing Science
University of Glasgow
Glasgow, United Kingdom
hopfgarf@dcs.gla.ac.uk
Joemon Jose
Department of Computing Science
University of Glasgow
Glasgow, United Kingdom
jj@dcs.gla.ac.uk
Abstract
In this paper, we propose a framework for recording,
analysing, indexing and retrieving news videos such as the
BBC One O’Clock News. We believe that such a framework
will be useful to identify implicit indicators of relevance,
a nearly untouched area in adaptive multimedia retrieval.
Due to its advantages as a web application and its up-to-
date content, it can be a promising approach to motivate a
broad quantity of users to interact with the system.
1. Introduction
With the improving capabilities of current hardware sys-
tems, there are ever growing possibilities to store and ma-
nipulate videos in a digital format, leading to a growing
number of video archives. People build their own digital
libraries from materials created through digital cameras and
camcorders, and use systems such as YouTube1 and Google
Video2 to place this material on the web. Unfortunately,
this data creation prowess is not matched by any compara-
ble tools to organise and retrieve video information.
There is a need to create new retrieval engines to assist
the user in searching and finding video scenes they would
like to see from many different video files. Unlike text re-
trieval systems, retrieval on digital video libraries is facing a
serious problem: The Semantic Gap. This is the difference
between the low-level data representation of videos and the
higher level concepts a user associates with video.
One way to address this problem is to use the interaction
between users and the system. There are different types of
interactions, usually divided into two categories: explicit
and implicit feedback. Explicit feedback is given when a
1http://www.youtube.com/
2http://video.google.com/
user informs a system what it has to do on purpose, such
as selecting something and marking it as relevant. Implicit
feedback is given unconsciously. An example is printing
out a web page, which may indicate an interest in that web
page. The basic assumption is that during a search, users’
actions are aimed to maximise the retrieval of relevant in-
formation. Implicit indicators have been used and analysed
in other domains, such as the WWW [1] and text retrieval
[10, 4], but as far as we know, not in the multimedia do-
main. However, traditional issues of implicit feedback can
be addressed in video retrieval since digital video libraries
facilitate more interaction and are hence amenable to im-
plicit feedback. And as [3] indicate, implicit feedback can
improve retrieval in digital video library retrieval systems.
Therefore, we propose to develop a web based retrieval sys-
tem for news video data which can be used to investigate
further the relevance of implicit indicators. Our aim is to
identify those implicit features which can positively influ-
ence video retrieval. To motivate a large number of differ-
ent users to interact with the system on a long-term basis, it
is necessary to provide a system including up-to-date news.
Hence, we propose a framework to record, analyse and in-
dex local broadcasting news such as the BBC News. This
paper is organised as follows: A brief introduction into web
based news video retrieval is given in Section 2. The pro-
posed framework will be introduced in Section 3: Section
3.1 proposes a framework of capturing, analysing and in-
dexing broadcast news and Section 3.2 will introduce the
retrieval backend, which shall also include user profiles to
improve retrieval. We propose to implement a web appli-
cation which will enable us to create different interfaces for
different environments which will be introduced in Section
3.3. Section 4 will summarise the proposal.
Second International Workshop on Semantic Media Adaptation and Personalization
0-7695-3040-0/07 $25.00 © 2007 IEEE
DOI 10.1109/SMAP.2007.12
233
Authorized licensed use limited to: UNIVERSITY OF GLASGOW. Downloaded on May 25, 2009 at 07:10 from IEEE Xplore.  Restrictions apply.
2. Background
In recent years, news videos have been the main focus of
research in the field of interactive video retrieval. A main
reason for this concentration on this domain is the inter-
national TRECVID [9] workshop, which provided a large
corpus of news videos in the last few years.
One of the most successful competitor of this work-
shop is the Fı´schla´r Digital Video System. The main in-
terest of the developers is to study techniques to provide
content-based navigation through digital video collection.
Therefore, Smeaton et al. [8] record the daily evening news
from the national’s main broadcasting station and automat-
ically segment news story boundaries. Their system pro-
vides story based news searching for the users. The web
based interface of this system can be accessed by using a
web browser, either Internet Explorer or Netscape, with an
ORACLE plug-in for video streaming.
Pickering et al. [7] focus their research on content-based
access to news videos in extracting keyframes and sum-
marising shots to stories based on the similarity of the cap-
tured teletext of each shot. Their web based interface pro-
vides browsing and seeking facilities. Videos can be played
using the RealPlayer.
Morrison and Jose [5] introduce the web based news
video retrieval system VideoSqueak. They record the BBC
One evening news and use the captured subtitles of the
broadcast as retrieval source. In [5], they evaluate different
presentation strategies for multimedia retrieval. A further
investigation in user behaviour has been ignored.
3. A News Video Retrieval Framework
A common approach to study the user’s behaviour of in-
teracting with a computer system is the analysis of transac-
tion logfiles. Such an analysis shall help to identify good
implicit indicators of relevance, as it can help to answer ba-
sic questions: What did the user do to find the information
he/she wanted? Can the user behaviour be used to improve
retrieval results? To get an unsophisticated impression of
users’ behaviour when interacting with a video retrieval sys-
tem, two main criteria can be stressed out: A large quantity
of different users interacting with the system is necessary
to generalise the results of a logfile analysis. Besides, non-
expert users should be interacting with the system, as they
will interact in a more intuitive way than expert users.
To investigate further into the influence of implicit in-
dicators of relevance, we propose to tackle these two cri-
teria in implementing a web based video retrieval system.
Web technology will enable us to spread the system to a
large population, e.g. all students on the University campus.
However, for offering an attractive news video retrieval sys-
tem to the general public, the system should be based on a
up-to-date news video corpus. Hence, we propose to record,
analyse and index daily broadcasting news such as the BBC
One O’Clock News. The proposed system architecture will
be introduced in the following.
3.1. Capturing and Indexing Process
Figure 1. System architecture of the captur-
ing and indexing process
Video Stream
Text (Subtitles)
BBC Scotland
extract visual 
features Visual Index
Text Index
Video Indexes
MPEG-1 Videos
capture
demux
demux
Video Segments
Keyframes
Content Analysis
model correlations
and index
annotate
Figure 1 illustrates the proposed architecture for record-
ing, analysing and indexing news video broadcasted by
BBC Scotland. Later on, the architecture could be expanded
to other news broadcasters.
First of all, the aerial broadcasting signal has to be cap-
tured, digitalised and stored on a server. The captured video
stream consists of audio, visual and textual (subtitle) data
which has to be demultiplexed. Due to technical conditions,
we have to capture the analogue signal and store both audio
and visual data in MPEG-1 format. The next step will be
the content analysis, which includes the separation of the
news video into segments and the extraction of appropri-
ate keyframes. These keyframes will be the retrieval source
for query-by-example retrieval. Hence, the visual features
of these images have to be extracted and stored on a server.
The standardised MPEG-7 format lends itself to it. In a next
step, the keyframes will be annotated based on their visual
features.
The subtitles need to be indexed. Therefore, they need
to be segmented based on the same time code of subtitle
and detected video shot. Besides, external sources such
as WordNet [2] can be used to model correlation of the
text. The Terrier Retrieval System [6] can be used for in-
dexing the subtitles. Terrier is a Java based framework for
the rapid development of large-scale information retrieval
applications and provides indexing and retrieval functional-
ities.
3.2. The Adaptive Retrieval System Back-
end
As we need many users interacting with our retrieval sys-
tem to study the effect of implicit relevance feedback, we
234
Authorized licensed use limited to: UNIVERSITY OF GLASGOW. Downloaded on May 25, 2009 at 07:10 from IEEE Xplore.  Restrictions apply.
propose to implement a web based application. Using the
AJAX technology will enable us to develop rich interfaces
which will run on conventional web browsers. This takes
away the burden of installing additional software on each
client. Besides, thanks to the Web 2.0 hype, users get used
to interacting with complex applications using their browser
only. This might motivate them to use the system on a reg-
ular basis to retrieve broadcasting news. Figure 2 provides
an overview of the proposed backend architecture. The re-
trieval system backend and the interface frontend are sepa-
rated from each other. This approach enables us to develop
and study different interface designs that provide different
implicit relevance feedback techniques but share the same
retrieval backend. Examples are given in Section 3.3.
Figure 2. System architecture of the adaptive
retrieval system
      Webserver
Visual Index
Text Index
Video Indexes
Retrieval Backend
Java API
Search and retrieval 
backend system
Web interface (servlet)
Video Segments
Keyframes
User Profile
Web interface (servlet) Web interface (servlet)
Mobile Device TV remote control
Desktop PC
The retrieval backend will run as a Java application on
the web server and will access the video indexes. It will
communicate with the interfaces, which will run as servlets.
Both retrieval engine and interface will create and include
user profiles to improve the retrieval performances. Each
client will access the system using a web browser.
3.3. Environments
The importance of implicit indicators of relevance be-
comes apparent when interfaces which are designed for dif-
ferent user interaction environments shall be designed. In
the following, we introduce three different interaction envi-
ronments and discuss their restrictions and advantages for
the user:
• Desktop Computers: The most familiar environment
for the user to do video retrieval is probably a stan-
dard desktop computer. Most adaptive video retrieval
systems have been designed to run under such envi-
ronment. The interface can be displayed on the screen
and users can easily interact with the system in using
the keyboard or mouse. One can assume that users
will take advantage of this interaction and hence give a
high quantity of implicit feedback. From today’s point
of view, this environment offers the highest amount
of possible implicit relevance feedback, such as query
reformulation, browsing results and sliding through a
video.
• Mobile Devices: In recent years, mobile devices have
evolved from simple telephones to complex devices
which are fully capable of handling multimedia con-
tent. Devices provide facilities to process multimedia
data or browse the internet. Such devices, however,
face two major problems: They are rather small and
more complicate to handle than a desktop computer.
The size of the screen does not provide much space
for more complex interfaces. Besides, the lack of a
real keyboard can be seen as a burden for the user to
provide further keywords for retrieval. One can con-
clude that such an environment implies the use of a
simple structured interface which does not force the
user to enter many search terms. Hence, a user will
have less possibilities to give implicit relevance feed-
back, for example query reformulation might be less
important due to complicated handling of the devices.
• TV: A widely accepted medium for multimedia con-
sumption is the television. Watching television, how-
ever, is a passive procedure. Viewers can select a pro-
gramme using a remote control, changing the content
is not possible though. Recently, Interactive TV, is be-
coming more and more popular. Using a remote con-
trol, viewers can interact directly when watching tele-
vision, e.g. in participating in quiz shows. In news
video retrieval, this limited interaction is a challenge.
It will be more complex to enter query terms, e.g. in
using the channel selection buttons as it is common for
mobile telephones. Hence, users will possibly avoid
to enter key words. On the other hand, the selection
keys and a display on the remote control provide a
method to give explicit relevance feedback. An ex-
ample: The viewer sees a video segment on television.
Now, he/she uses the remote control to judge the rele-
vance of this segment.
4. Summary
Implicit relevance feedback has been a successful ap-
proach in the textual domain to improve retrieval results.
In the video data domain, however, this approach has rarely
been studied. Therefore, we propose to develop a retrieval
system for news videos which shall help to shed light on the
possibility of implicit factors for retrieval. The news video
235
Authorized licensed use limited to: UNIVERSITY OF GLASGOW. Downloaded on May 25, 2009 at 07:10 from IEEE Xplore.  Restrictions apply.
retrieval framework will include recording, analysing and
indexing of news broadcasts such as the BBC One O’Clock
News. Furthermore, we propose to develop web-based re-
trieval interfaces. A transaction logfile analysis of the users’
interaction with the interface shall help to identify implicit
indicators of relevance. To generalise the results of such an
analysis, a large scale user population is needed. Using web
technology will help us to address a broad number of users
as web browsers can be found on nearly all computers and
are more and more used for complex applications.
5 Acknowledgments
This research was supported by the European Commis-
sion under the contract FP6-027026-K-SPACE. It is the
view of the authors but not necessarily the view of the com-
munity.
References
[1] M. Claypool, P. Le, M. Wased, and D. Brown. Implicit inter-
est indicators. In Intelligent User Interfaces, pages 33–40,
2001.
[2] C. Fellbaum, editor. WordNet: An Electronic Lexical
Database. The MIT Press, Cambridge, MA, 1998.
[3] F. Hopfgartner and J. Jose. Evaluating the Implicit Feed-
back Models for Adaptive Video Retrieval. In Proceedings
of the International Workshop on Multimedia Information
Retrieval (MIR 2007), Augsburg, Germany, 2007.
[4] D. Kelly and J. Teevan. Implicit feedback for inferring user
preference: A bibliography. SIGIR Forum, 32(2), 2003.
[5] S. Morrison and J. Jose. A comparative study of online
news retrieval and presentation strategies. In ISMSE ’04:
Proceedings of the IEEE Sixth International Symposium on
Multimedia Software Engineering (ISMSE’04), pages 403–
409, Washington, DC, USA, 2004. IEEE Computer Society.
[6] I. Ounis, G. Amati, V. Plachouras, B. He, C. Macdonald, and
D. Johnson. Terrier Information Retrieval Platform. In Pro-
ceedings of the 27th European Conference on Information
Retrieval (ECIR 05), Santiago de Compostela, Spain, pages
517–519, 2005.
[7] M. J. Pickering, L. Wong, and S. Ru¨ger. ANSES: Summari-
sation of news video. Image and Video Retrieval, 2788:481–
486, 2003.
[8] A. F. Smeaton. The Fı´schla´r Digital Library: Networked
Access to a Video Archive of TV News. In TERENA Net-
working Conference 2002, Limerick, Ireland, 3-6 June 2002,
2002.
[9] A. F. Smeaton, P. Over, and W. Kraaij. Evaluation cam-
paigns and TRECVid. In MIR ’06: Proceedings of the
8th ACM International Workshop on Multimedia Informa-
tion Retrieval, pages 321–330, New York, NY, USA, 2006.
ACM Press.
[10] R. White, J. Jose, C. van Rijsbergen, and I. Ruthven. A Sim-
ulated Study of Implicit Feedback Models. In Proceedings
of the 26th European Conference on Information Retrieval
Research (ECIR ’04). Lecture Notes in Computer Science,
pages 311–326, 2004.
236
Authorized licensed use limited to: UNIVERSITY OF GLASGOW. Downloaded on May 25, 2009 at 07:10 from IEEE Xplore.  Restrictions apply.


A news video retrieval framework for the study of implicit relevance feedback.

Crossref

A News Video Retrieval Framework for the Study of Implicit Relevance Feedback

Enlighten

       Hopfgartner, F. and Jose, J.M. (2007) A news video retrieval framework for the study of implicit relevance. In: Second International Workshop on Semantic Media Adaptation and Personalization, 17-18 Dec 2007, London, UK.   http://eprints.gla.ac.uk/5752/  Deposited on: 25 May 2009   Enlighten – Research publications by members of the University of Glasgow http://eprints.gla.ac.uk A News Video Retrieval Framework for the Study of Implicit RelevanceFeedbackFrank HopfgartnerDepartment of Computing ScienceUniversity of GlasgowGlasgow, United Kingdomhopfgarf@dcs.gla.ac.ukJoemon JoseDepartment of Computing ScienceUniversity of GlasgowGlasgow, United Kingdomjj@dcs.gla.ac.ukAbstractIn this paper, we propose a framework for recording,analysing, indexing and retrieving news videos such as theBBC One O’Clock News. We believe that such a frameworkwill be useful to identify implicit indicators of relevance,a nearly untouched area in adaptive multimedia retrieval.Due to its advantages as a web application and its up-to-date content, it can be a promising approach to motivate abroad quantity of users to interact with the system.1. IntroductionWith the improving capabilities of current hardware sys-tems, there are ever growing possibilities to store and ma-nipulate videos in a digital format, leading to a growingnumber of video archives. People build their own digitallibraries from materials created through digital cameras andcamcorders, and use systems such as YouTube1 and GoogleVideo2 to place this material on the web. Unfortunately,this data creation prowess is not matched by any compara-ble tools to organise and retrieve video information.There is a need to create new retrieval engines to assistthe user in searching and finding video scenes they wouldlike to see from many different video files. Unlike text re-trieval systems, retrieval on digital video libraries is facing aserious problem: The Semantic Gap. This is the differencebetween the low-level data representation of videos and thehigher level concepts a user associates with video.One way to address this problem is to use the interactionbetween users and the system. There are different types ofinteractions, usually divided into two categories: explicitand implicit feedback. Explicit feedback is given when a1http://www.youtube.com/2http://video.google.com/user informs a system what it has to do on purpose, suchas selecting something and marking it as relevant. Implicitfeedback is given unconsciously. An example is printingout a web page, which may indicate an interest in that webpage. The basic assumption is that during a search, users’actions are aimed to maximise the retrieval of relevant in-formation. Implicit indicators have been used and analysedin other domains, such as the WWW [1] and text retrieval[10, 4], but as far as we know, not in the multimedia do-main. However, traditional issues of implicit feedback canbe addressed in video retrieval since digital video librariesfacilitate more interaction and are hence amenable to im-plicit feedback. And as [3] indicate, implicit feedback canimprove retrieval in digital video library retrieval systems.Therefore, we propose to develop a web based retrieval sys-tem for news video data which can be used to investigatefurther the relevance of implicit indicators. Our aim is toidentify those implicit features which can positively influ-ence video retrieval. To motivate a large number of differ-ent users to interact with the system on a long-term basis, itis necessary to provide a system including up-to-date news.Hence, we propose a framework to record, analyse and in-dex local broadcasting news such as the BBC News. Thispaper is organised as follows: A brief introduction into webbased news video retrieval is given in Section 2. The pro-posed framework will be introduced in Section 3: Section3.1 proposes a framework of capturing, analysing and in-dexing broadcast news and Section 3.2 will introduce theretrieval backend, which shall also include user profiles toimprove retrieval. We propose to implement a web appli-cation which will enable us to create different interfaces fordifferent environments which will be introduced in Section3.3. Section 4 will summarise the proposal.Second International Workshop on Semantic Media Adaptation and Personalization0-7695-3040-0/07 $25.00 © 2007 IEEEDOI 10.1109/SMAP.2007.12233Authorized licensed use limited to: UNIVERSITY OF GLASGOW. Downloaded on May 25, 2009 at 07:10 from IEEE Xplore.  Restrictions apply.2. BackgroundIn recent years, news videos have been the main focus ofresearch in the field of interactive video retrieval. A mainreason for this concentration on this domain is the inter-national TRECVID [9] workshop, which provided a largecorpus of news videos in the last few years.One of the most successful competitor of this work-shop is the Fı´schla´r Digital Video System. The main in-terest of the developers is to study techniques to providecontent-based navigation through digital video collection.Therefore, Smeaton et al. [8] record the daily evening newsfrom the national’s main broadcasting station and automat-ically segment news story boundaries. Their system pro-vides story based news searching for the users. The webbased interface of this system can be accessed by using aweb browser, either Internet Explorer or Netscape, with anORACLE plug-in for video streaming.Pickering et al. [7] focus their research on content-basedaccess to news videos in extracting keyframes and sum-marising shots to stories based on the similarity of the cap-tured teletext of each shot. Their web based interface pro-vides browsing and seeking facilities. Videos can be playedusing the RealPlayer.Morrison and Jose [5] introduce the web based newsvideo retrieval system VideoSqueak. They record the BBCOne evening news and use the captured subtitles of thebroadcast as retrieval source. In [5], they evaluate differentpresentation strategies for multimedia retrieval. A furtherinvestigation in user behaviour has been ignored.3. A News Video Retrieval FrameworkA common approach to study the user’s behaviour of in-teracting with a computer system is the analysis of transac-tion logfiles. Such an analysis shall help to identify goodimplicit indicators of relevance, as it can help to answer ba-sic questions: What did the user do to find the informationhe/she wanted? Can the user behaviour be used to improveretrieval results? To get an unsophisticated impression ofusers’ behaviour when interacting with a video retrieval sys-tem, two main criteria can be stressed out: A large quantityof different users interacting with the system is necessaryto generalise the results of a logfile analysis. Besides, non-expert users should be interacting with the system, as theywill interact in a more intuitive way than expert users.To investigate further into the influence of implicit in-dicators of relevance, we propose to tackle these two cri-teria in implementing a web based video retrieval system.Web technology will enable us to spread the system to alarge population, e.g. all students on the University campus.However, for offering an attractive news video retrieval sys-tem to the general public, the system should be based on aup-to-date news video corpus. Hence, we propose to record,analyse and index daily broadcasting news such as the BBCOne O’Clock News. The proposed system architecture willbe introduced in the following.3.1. Capturing and Indexing ProcessFigure 1. System architecture of the captur-ing and indexing processVideo StreamText (Subtitles)BBC Scotlandextract visual features Visual IndexText IndexVideo IndexesMPEG-1 VideoscapturedemuxdemuxVideo SegmentsKeyframesContent Analysismodel correlationsand indexannotateFigure 1 illustrates the proposed architecture for record-ing, analysing and indexing news video broadcasted byBBC Scotland. Later on, the architecture could be expandedto other news broadcasters.First of all, the aerial broadcasting signal has to be cap-tured, digitalised and stored on a server. The captured videostream consists of audio, visual and textual (subtitle) datawhich has to be demultiplexed. Due to technical conditions,we have to capture the analogue signal and store both audioand visual data in MPEG-1 format. The next step will bethe content analysis, which includes the separation of thenews video into segments and the extraction of appropri-ate keyframes. These keyframes will be the retrieval sourcefor query-by-example retrieval. Hence, the visual featuresof these images have to be extracted and stored on a server.The standardised MPEG-7 format lends itself to it. In a nextstep, the keyframes will be annotated based on their visualfeatures.The subtitles need to be indexed. Therefore, they needto be segmented based on the same time code of subtitleand detected video shot. Besides, external sources suchas WordNet [2] can be used to model correlation of thetext. The Terrier Retrieval System [6] can be used for in-dexing the subtitles. Terrier is a Java based framework forthe rapid development of large-scale information retrievalapplications and provides indexing and retrieval functional-ities.3.2. The Adaptive Retrieval System Back-endAs we need many users interacting with our retrieval sys-tem to study the effect of implicit relevance feedback, we234Authorized licensed use limited to: UNIVERSITY OF GLASGOW. Downloaded on May 25, 2009 at 07:10 from IEEE Xplore.  Restrictions apply.propose to implement a web based application. Using theAJAX technology will enable us to develop rich interfaceswhich will run on conventional web browsers. This takesaway the burden of installing additional software on eachclient. Besides, thanks to the Web 2.0 hype, users get usedto interacting with complex applications using their browseronly. This might motivate them to use the system on a reg-ular basis to retrieve broadcasting news. Figure 2 providesan overview of the proposed backend architecture. The re-trieval system backend and the interface frontend are sepa-rated from each other. This approach enables us to developand study different interface designs that provide differentimplicit relevance feedback techniques but share the sameretrieval backend. Examples are given in Section 3.3.Figure 2. System architecture of the adaptiveretrieval system      WebserverVisual IndexText IndexVideo IndexesRetrieval BackendJava APISearch and retrieval backend systemWeb interface (servlet)Video SegmentsKeyframesUser ProfileWeb interface (servlet) Web interface (servlet)Mobile Device TV remote controlDesktop PCThe retrieval backend will run as a Java application onthe web server and will access the video indexes. It willcommunicate with the interfaces, which will run as servlets.Both retrieval engine and interface will create and includeuser profiles to improve the retrieval performances. Eachclient will access the system using a web browser.3.3. EnvironmentsThe importance of implicit indicators of relevance be-comes apparent when interfaces which are designed for dif-ferent user interaction environments shall be designed. Inthe following, we introduce three different interaction envi-ronments and discuss their restrictions and advantages forthe user:• Desktop Computers: The most familiar environmentfor the user to do video retrieval is probably a stan-dard desktop computer. Most adaptive video retrievalsystems have been designed to run under such envi-ronment. The interface can be displayed on the screenand users can easily interact with the system in usingthe keyboard or mouse. One can assume that userswill take advantage of this interaction and hence give ahigh quantity of implicit feedback. From today’s pointof view, this environment offers the highest amountof possible implicit relevance feedback, such as queryreformulation, browsing results and sliding through avideo.• Mobile Devices: In recent years, mobile devices haveevolved from simple telephones to complex deviceswhich are fully capable of handling multimedia con-tent. Devices provide facilities to process multimediadata or browse the internet. Such devices, however,face two major problems: They are rather small andmore complicate to handle than a desktop computer.The size of the screen does not provide much spacefor more complex interfaces. Besides, the lack of areal keyboard can be seen as a burden for the user toprovide further keywords for retrieval. One can con-clude that such an environment implies the use of asimple structured interface which does not force theuser to enter many search terms. Hence, a user willhave less possibilities to give implicit relevance feed-back, for example query reformulation might be lessimportant due to complicated handling of the devices.• TV: A widely accepted medium for multimedia con-sumption is the television. Watching television, how-ever, is a passive procedure. Viewers can select a pro-gramme using a remote control, changing the contentis not possible though. Recently, Interactive TV, is be-coming more and more popular. Using a remote con-trol, viewers can interact directly when watching tele-vision, e.g. in participating in quiz shows. In newsvideo retrieval, this limited interaction is a challenge.It will be more complex to enter query terms, e.g. inusing the channel selection buttons as it is common formobile telephones. Hence, users will possibly avoidto enter key words. On the other hand, the selectionkeys and a display on the remote control provide amethod to give explicit relevance feedback. An ex-ample: The viewer sees a video segment on television.Now, he/she uses the remote control to judge the rele-vance of this segment.4. SummaryImplicit relevance feedback has been a successful ap-proach in the textual domain to improve retrieval results.In the video data domain, however, this approach has rarelybeen studied. Therefore, we propose to develop a retrievalsystem for news videos which shall help to shed light on thepossibility of implicit factors for retrieval. The news video235Authorized licensed use limited to: UNIVERSITY OF GLASGOW. Downloaded on May 25, 2009 at 07:10 from IEEE Xplore.  Restrictions apply.retrieval framework will include recording, analysing andindexing of news broadcasts such as the BBC One O’ClockNews. Furthermore, we propose to develop web-based re-trieval interfaces. A transaction logfile analysis of the users’interaction with the interface shall help to identify implicitindicators of relevance. To generalise the results of such ananalysis, a large scale user population is needed. Using webtechnology will help us to address a broad number of usersas web browsers can be found on nearly all computers andare more and more used for complex applications.5 AcknowledgmentsThis research was supported by the European Commis-sion under the contract FP6-027026-K-SPACE. It is theview of the authors but not necessarily the view of the com-munity.References[1] M. Claypool, P. Le, M. Wased, and D. Brown. Implicit inter-est indicators. In Intelligent User Interfaces, pages 33–40,2001.[2] C. Fellbaum, editor. WordNet: An Electronic LexicalDatabase. The MIT Press, Cambridge, MA, 1998.[3] F. Hopfgartner and J. Jose. Evaluating the Implicit Feed-back Models for Adaptive Video Retrieval. In Proceedingsof the International Workshop on Multimedia InformationRetrieval (MIR 2007), Augsburg, Germany, 2007.[4] D. Kelly and J. Teevan. Implicit feedback for inferring userpreference: A bibliography. SIGIR Forum, 32(2), 2003.[5] S. Morrison and J. Jose. A comparative study of onlinenews retrieval and presentation strategies. In ISMSE ’04:Proceedings of the IEEE Sixth International Symposium onMultimedia Software Engineering (ISMSE’04), pages 403–409, Washington, DC, USA, 2004. IEEE Computer Society.[6] I. Ounis, G. Amati, V. Plachouras, B. He, C. Macdonald, andD. Johnson. Terrier Information Retrieval Platform. In Pro-ceedings of the 27th European Conference on InformationRetrieval (ECIR 05), Santiago de Compostela, Spain, pages517–519, 2005.[7] M. J. Pickering, L. Wong, and S. Ru¨ger. ANSES: Summari-sation of news video. Image and Video Retrieval, 2788:481–486, 2003.[8] A. F. Smeaton. The Fı´schla´r Digital Library: NetworkedAccess to a Video Archive of TV News. In TERENA Net-working Conference 2002, Limerick, Ireland, 3-6 June 2002,2002.[9] A. F. Smeaton, P. Over, and W. Kraaij. Evaluation cam-paigns and TRECVid. In MIR ’06: Proceedings of the8th ACM International Workshop on Multimedia Informa-tion Retrieval, pages 321–330, New York, NY, USA, 2006.ACM Press.[10] R. White, J. Jose, C. van Rijsbergen, and I. Ruthven. A Sim-ulated Study of Implicit Feedback Models. In Proceedingsof the 26th European Conference on Information RetrievalResearch (ECIR ’04). Lecture Notes in Computer Science,pages 311–326, 2004.236Authorized licensed use limited to: UNIVERSITY OF GLASGOW. Downloaded on May 25, 2009 at 07:10 from IEEE Xplore.  Restrictions apply.

A comparative study of online news retrieval and presentation strategies.

A Simulated Study of Implicit Feedback Models.

ANSES: Summarisation of news video. Image and Video Retrieval,

Evaluating the Implicit Feedback Models for Adaptive Video Retrieval.

Evaluation campaigns and TRECVid.

Implicit feedback for inferring user preference: A bibliography.

Implicit interest indicators.

Terrier Information Retrieval Platform.

The F´ ıschl´ ar Digital Library: Networked Access to a Video Archive of TV News.

WordNet: An Electronic Lexical Database.

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.144.2802

A news video retrieval framework for the study of implicit relevance feedback.

Abstract

Similar works

Full text

Available Versions

Enlighten: Publications

Crossref

Enlighten