In this paper we review the NTCIR12-Lifelog pilot task,

which ran at NTCIR-12. We outline the test collection employed,

along with the tasks, the eight submissions and the

findings from this pilot task. We finish by suggesting future

plans for the task

Albatal, Rami

Gurrin, Cathal

Hopfgartner, Frank

Joho, Hideo

Zhou, Liting

English

In this paper we review the NTCIR12-Lifelog pilot task,
which ran at NTCIR-12. We outline the test collection employed, along with the tasks, the eight submissions and the
findings from this pilot task. We finish by suggesting future
plans for the task

Irish Universities

Overview of NTCIR-12 Lifelog Task

In this paper we review the NTCIR12-Lifelog pilot task,

which ran at NTCIR-12. We outline the test collection employed, along with the tasks, the eight submissions and the

findings from this pilot task. We finish by suggesting future

plans for the task

DCU Online Research Access Service

Enlighten

Overview of NTCIR-12 Lifelog TaskCathal GurrinDublin City Universitycathal.gurrin@dcu.ieHideo JohoUniversity of Tsukubahideo@slis.tsukuba.ac.jpFrank HopfgartnerUniversity of Glasgowfrank.hopfgartner@glasgow.ac.ukLiting ZhouDublin City Universitybecky.zhou@dcu.ieRami AlbatalHeystaks Technologies Ltd.rami.albatal@heystaks.comABSTRACTIn this paper we review the NTCIR12-Lifelog pilot task,which ran at NTCIR-12. We outline the test collection em-ployed, along with the tasks, the eight submissions and thefindings from this pilot task. We finish by suggesting futureplans for the task.SubtasksLifelog Semantic Access Task (LSAT)Lifelog Insight Task (LIT)Keywordslifelog, test collection, information retrieval, multimodal, eval-uation1. INTRODUCTIONOne aspect of Information Retrieval that has been gather-ing increasing attention in recent years is the concept of lifel-ogging. Lifelogging is defined as “a form of pervasive com-puting, consisting of a unified digital record of the totality ofan individual’s experiences, captured multi-modally throughdigital sensors and stored permanently as a personal multi-media archive” [5]. Lifelogging typically generates multime-dia archives of life-experience data in an enormous (poten-tially multi-decade) lifelog. However, lifelogging has neverbeen the subject of a rigorous comparative benchmarkingexercise, even though there have been calls for a test collec-tion of lifelog data [5, 9].In this paper we describe the NTCIR12-Lifelog pilot task.We begin with a description of the requirements for thelifelog test collection, followed by a description of the testcollection itself. We then describe the two sub-tasks thatwere organised for this pilot task, before outlining the eightsubmissions and the results of these submissions. Finallywe outline plans for the next edition of the Lifelog task atNTCIR.2. TASK OVERVIEWThis pilot lifelog task aims to begin the comparative eval-uation of information access and retrieval systems operat-ing over personal lifelog data. This task includes of twosub-tasks, both (or either) could have been participated inindependently. The two sub-tasks were:• Lifelog Semantic Access Task (LSAT) to explore searchand retrieval from lifelogs, and• Lifelog Insight Task (LIT) to explore knowledge min-ing and visualisation of lifelogs.2.1 LSAT TaskThe LSAT task was a typical known-item search task ap-plied over lifelog data. In this subtask, the participants hadto retrieve a number of specific moments in a lifelogger’slife. We consider moments to be semantic events, or activi-ties that happened at least once in the dataset. The task canbest be compared to a known-item search task with one (ormore) relevant items. Participants were allowed to under-take the LAST task in an interactive or automatic manner.For interactive submissions, a maximum of five minutes ofsearch time was allowed per topic. The LSAT task included48 search tasks, generated by the lifeloggers and guided byKahneman’s lifestyle activities [10]. In total, the LSAT taskreceived 5 submissions.2.2 LIT TaskThe LIT task was exploratory in nature and the aim of thissubtask was to gain insights into the lifelogger’s daily life ac-tivities. It followed the idea of the Quantified Self movementthat focuses on the visualization of knowledge mined fromself-tracking data to provide “self-knowledge through num-bers”. Participants were requested to provide insights aboutthe lifelog data that support the lifelogger in the act of re-flecting upon the data, facilitate filtering and provide for ef-ficient/effective means of visualisation of the data. The LITtask included ten information needs representing the ideathat one would use a lifelog as a source for self-reflection. Wedid not intend to have an explicit evaluation for this task,rather we expected all participants to being their demon-strations or reflective output at the NTCIR conference.3. DESCRIPTION OF THE LIFELOG TESTCOLLECTIONThe lifelog test collection described in this paper was de-veloped for the Lifelog track of the NTCIR-12 evaluationforum. Prior to generating the test collection we defined anumber of requirements for the collection:• To be large enough to support a number of differentretrieval tasks, but not so large as to discourage par-ticipation and use.• To lower barriers-to-participation by including suffi-cient metadata, so that researchers interested in a broadrange of applications, with a range of expertise, canutilise the test collection.Topic Title Total Relevant Recall (Automatic) Recall (Interactive)The Red Taxi 1 1 1Photographing a Lake 2 2 N/APresenting/Lecturing 3 2 0Tower Bridge 1 1 1Driving a Rental Car 19 0 5Attending a Lecture 1 0 0Eating while in Conversation 1 1 1On the Bus or Train 4 2 1New Key 1 0 1Having a Drink 2 0 1Lost 1 1 N/ARiding a Red Train 3 2 3Man in a Burberry Coat 1 1 N/AThe Church 1 1 1The Rugby Match 3 1 3Costa Coffee 4 2 2Antiques Store 3 3 3Outdoor Computing 2 0 1Building a Computer 14 1 1Airbus A380 1 1 1Shopping for a Bottle of Wine 1 1 N/AATM 3 3 1Shopping For Fish 3 2 3Repairing a Car Wheel 1 0 1Cycling home 7 0 5Happy Homework 1 0 N/AShopping 14 9 5Informal Coffee Meeting 1 1 N/ALunchtime 8 4 0In a Meeting 8 4 1Bus to the Airport 1 1 1A Movie on the Flight 1 1 0The Metro 12 9 3A Garden Chat with Dog 1 1 1Lion at the Gate 1 1 1Checking the Menu 3 0 0The BirdaˆA˘Z´s Nest Stadium 3 2 1Watching TV 21 0 2Grocery Store 39 11 18Strolling on the Deck 1 0 N/AEating on the Roadside 1 0 1Writing 51 4 1The Elevator 19 7 7Car Repair 1 0 1Drinking in a Pub 18 6 11Barbershop 1 1 N/ALottery 1 1 N/ACheckout 30 2 4Table 2: Statistical Analysis of NTCIR-12 Lifelog Datadescriptions and sentiment analysis to identify negative fea-ture keywords. A final submission utilised query expansionon every keyword in an attempt to enhance retrieval perfor-mance.LIG-MRM, France. The LIG-MRM group (LIG-MIRM inFigure 5 and Automatic in Figure 6) focused on enhancingthe performance of the visual concept detectors to be usedfor retrieval, and not relying on the CAFFE classifier out-put [12]. This was achieved using three techniques, includ-ing Dynamic Convolutional Neural Networks VGG (1000dimensional features of ImageNet), a classification providedby their own MSVM on optimized data TRECVid (346 di-mensional features), and a third approach utilising MSVMclassification on VOC concepts (20 dimensional features).The images were also described by their respective anno-tated metadata (when present), such as place (e.g. ”home”,”work”, etc.) and activity (e.g. ”walking”, ”transport”, ”bus”,etc.). When processing a topic Q, a mapping (string inclu-sion) of each word from Q into one or more visual conceptswas performed. The score of each image was then computedTime Elapsed Num. of Relevant Moments Found Number of Topics for which a Relevant Mo-ment was Found10s 7 330s 20 960s 32 15120s 56 27300s 94 34Table 3: Interactive Run Comparison over Timeinitial step towards this goal, the NTCIR Lifelog data wasmanually analysed from the viewpoint of individual sleepinghabits and in our paper we discuss possible approaches toleveraging such data for the next version of Sleep-flower.Toyohashi University, Japan. The group from Toyohashiexamined repeated pattern discovery from lifelog image se-quences, by applying a Spoken Term Discovery technique,which is an approach usually used to words from speechdata [16]. A variant of Dynamic Time Warping was usedin an experimental approach to extract extract meaningfulpatterns from the lifelog data. It is suggested that this couldbe a useful approach for insight generation from archives oflifelog data.Dublin City University, Ireland. The submission fromDublin City University introduced an interactive lifelog in-terrogation system which allowed for manual interrogationof the lifelog dataset for the occurrence of visual conceptsthat were assumed to match the information needs [3]. Theresults of this manual interrogation were then used to gen-erate insights and infographics for the provided topics.5. LEARNINGS / FUTURE PLANSThis was the first collaborative benchmarking exercise forlifelog data. It attracted eight participants, four for theautomatic LSAT search task, one for the interactive LSATsearch task and three for the LIT task. We can summarisethe learnings from this pilot task as follows:• There is still no standardised approach to retrieval oflifelog data; each of the participants in the LSAT tasktook different approaches to retrieval. This suggeststhat the LSAT task is valuable to run again in futureyears.• The dataset should contain more semantically rich datato support mare groups to take part. Such data shouldtry to capture the semantics of daily life, and not justthe activities.• The supplied metadata and visual concepts should beat a higher-level of quality. The positive effect of higher-quality metadata can be seen in the results of the best-performing automatic LSAT run.• The LSAT task is a valuable task, though effort shouldbe made to encourage more interactive participants.• The LSAT unit-of-retrieval being the moment/eventrequired the mapping of submitted image IDs to mo-ments that are defined by the coordinators in a manualprocess. This suggests that the unit of retrieval itselfcould become an interesting task, so we propose to in-clude an event-segmentation task in the next runningof the lifelog collaborative benchmarking exercise.6. CONCLUSIONIn this paper, we described the data and the activitiesfrom the first lifelog pilot-task at NTCIR. There were twosub-tasks, the LSAT known-item search task and the LITdata insights task. These task were undertaken by fiveand three participants respectively. The results attainedby the participants showed that although many differentapproaches for automatic retrieval were applied, the onethat appeared to work best was a computer-vision-basedapproach that augmented the provided CAFFE concept de-tector output with an enhanced concept detector based ona CNN-based model. In terms of interactive vs. automaticsearch, the interactive system performed better, as wouldbe expected. The LSAT task is a valuable task and shouldcontinue, though perhaps joined by additional tasks, such asevent segmentation. It is proposed that a bigger and moresemantically rich dataset be employed for any future lifelogcomparative benchmarking tasks.AcknowledgementsWe acknowledge the financial support of Science Founda-tion Ireland (SFI) under grant number SFI/12/RC/2289and the input of the DCU ethics committee and the risk& compliance officer. We acknowledge financial support bythe European Science Foundation via its Research NetworkProgramme “Evaluating Information Access Systems”. Theproject has been reviewed and approved by the ResearchEthics Committee of the College of Arts of University ofGlasgow.7. REFERENCES[1] A. Cavoukian. Privacy by design: The 7 foundationalprinciples. implementation and mapping of fairinformation practices. Information and PrivacyCommissioner of Ontario, Canada, 2010.[2] G. de Oliveira Barra, A. C. Ayala, M. BolaA˜s´os,M. Dimiccoli, M. Aghaei, M. CarnA˜l’, X. Giro-I-Nieto,and P. Radeva. Lemore: A lifelog engine for momentsretrieval at the ntcir-lifelog lsat task. In Proceedings ofNTCIR-12, Tokyo, Japan, 2016.[3] A. Duane, C. Gurrin, L. Zhou, and R. Gupta. Visualinsights from personal lifelogs - insight at the ntcir-12lifelog lit task. In Proceedings of NTCIR-12, Tokyo,Japan, 2016.[4] C. Gurrin, R. Albatal, H. Joho, and K. Ishii. Aprivacy by design approach to lifelogging. In DigitalEnlightenment Yearbook 2014, pages 49–73. IOS Press,2014.[5] C. Gurrin, A. F. Smeaton, and A. R. Doherty.Lifelogging: Personal big data. Foundations andTrends in Information Retrieval, 8(1):1–125, 2014.[6] S. Hodges, L. Williams, E. Berry, S. Izadi,J. Srinivasan, A. Butler, G. Smyth, N. Kapur, andK. R. Wood. Sensecam: A retrospective memory aid.In UbiComp 2006, pages 177–193, 2006.[7] S. Iijima and T. Sakai. Slll at the ntcir-12 lifelog task:Sleepflower and the lit subtask. In Proceedings ofNTCIR-12, Tokyo, Japan, 2016.[8] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev,J. Long, R. Girshick, S. Guadarrama, and T. Darrell.Caffe: Convolutional architecture for fast featureembedding. In ACM MM, pages 675–678, 2014.[9] G. J. Jones, C. Gurrin, L. Kelly, D. Byrne, andY. Chen. Information access tasks and evaluation forpersonal lifelogs. 2008.[10] D. Kahneman, A. B. Krueger, D. A. Schkade,N. Schwarz, and A. A. Stone. A survey method forcharacterizing daily life experience: The dayreconstruction method. Science, 306:1776–1780, 2004.[11] H. L. Lin, T. C. Chiang, L.-P. Chen, and P.-C. Yang.Image searching by events with deep learning forntcir-12 lifelog. In Proceedings of NTCIR-12, Tokyo,Japan, 2016.[12] B. Safadi, P. Mulhem, G. Qul’not, and J.-P. Chevallet.Mrim-lig at ntcir lifelog semantic access task. InProceedings of NTCIR-12, Tokyo, Japan, 2016.[13] H. Scells, G. Zuccon, and K. Kitto. Qut at the ntcirlifelog semantic access task. In Proceedings ofNTCIR-12, Tokyo, Japan, 2016.[14] A. J. Sellen and S. Whittaker. Beyond total capture:A constructive critique of lifelogging. Commun. ACM,53(5):70–77, May 2010.[15] L. Xia, Y. Ma, and W. Fan. Vtir at the ntcir-12 2016lifelog semantic access task. In Proceedings ofNTCIR-12, Tokyo, Japan, 2016.[16] K. Yamauchi and T. Akiba. Repeated event discoveryfrom image sequences by using segmental dynamictime warping: experiment at the ntcir-12 lifelog task.In Proceedings of NTCIR-12, Tokyo, Japan, 2016.

Enlighten: Publications

Overview of NTCIR-12 Lifelog Task
Cathal Gurrin
Dublin City University
cathal.gurrin@dcu.ie
Hideo Joho
University of Tsukuba
hideo@slis.tsukuba.ac.jp
Frank Hopfgartner
University of Glasgow
frank.hopfgartner@glasgow.ac.uk
Liting Zhou
Dublin City University
becky.zhou@dcu.ie
Rami Albatal
Heystaks Technologies Ltd.
rami.albatal@heystaks.com
ABSTRACT
In this paper we review the NTCIR12-Lifelog pilot task,
which ran at NTCIR-12. We outline the test collection em-
ployed, along with the tasks, the eight submissions and the
findings from this pilot task. We finish by suggesting future
plans for the task.
Subtasks
Lifelog Semantic Access Task (LSAT)
Lifelog Insight Task (LIT)
Keywords
lifelog, test collection, information retrieval, multimodal, eval-
uation
1. INTRODUCTION
One aspect of Information Retrieval that has been gather-
ing increasing attention in recent years is the concept of lifel-
ogging. Lifelogging is defined as “a form of pervasive com-
puting, consisting of a unified digital record of the totality of
an individual’s experiences, captured multi-modally through
digital sensors and stored permanently as a personal multi-
media archive” [5]. Lifelogging typically generates multime-
dia archives of life-experience data in an enormous (poten-
tially multi-decade) lifelog. However, lifelogging has never
been the subject of a rigorous comparative benchmarking
exercise, even though there have been calls for a test collec-
tion of lifelog data [5, 9].
In this paper we describe the NTCIR12-Lifelog pilot task.
We begin with a description of the requirements for the
lifelog test collection, followed by a description of the test
collection itself. We then describe the two sub-tasks that
were organised for this pilot task, before outlining the eight
submissions and the results of these submissions. Finally
we outline plans for the next edition of the Lifelog task at
NTCIR.
2. TASK OVERVIEW
This pilot lifelog task aims to begin the comparative eval-
uation of information access and retrieval systems operat-
ing over personal lifelog data. This task includes of two
sub-tasks, both (or either) could have been participated in
independently. The two sub-tasks were:
• Lifelog Semantic Access Task (LSAT) to explore search
and retrieval from lifelogs, and
• Lifelog Insight Task (LIT) to explore knowledge min-
ing and visualisation of lifelogs.
2.1 LSAT Task
The LSAT task was a typical known-item search task ap-
plied over lifelog data. In this subtask, the participants had
to retrieve a number of specific moments in a lifelogger’s
life. We consider moments to be semantic events, or activi-
ties that happened at least once in the dataset. The task can
best be compared to a known-item search task with one (or
more) relevant items. Participants were allowed to under-
take the LAST task in an interactive or automatic manner.
For interactive submissions, a maximum of five minutes of
search time was allowed per topic. The LSAT task included
48 search tasks, generated by the lifeloggers and guided by
Kahneman’s lifestyle activities [10]. In total, the LSAT task
received 5 submissions.
2.2 LIT Task
The LIT task was exploratory in nature and the aim of this
subtask was to gain insights into the lifelogger’s daily life ac-
tivities. It followed the idea of the Quantified Self movement
that focuses on the visualization of knowledge mined from
self-tracking data to provide “self-knowledge through num-
bers”. Participants were requested to provide insights about
the lifelog data that support the lifelogger in the act of re-
flecting upon the data, facilitate filtering and provide for ef-
ficient/effective means of visualisation of the data. The LIT
task included ten information needs representing the idea
that one would use a lifelog as a source for self-reflection. We
did not intend to have an explicit evaluation for this task,
rather we expected all participants to being their demon-
strations or reflective output at the NTCIR conference.
3. DESCRIPTION OF THE LIFELOG TEST
COLLECTION
The lifelog test collection described in this paper was de-
veloped for the Lifelog track of the NTCIR-12 evaluation
forum. Prior to generating the test collection we defined a
number of requirements for the collection:
• To be large enough to support a number of different
retrieval tasks, but not so large as to discourage par-
ticipation and use.
• To lower barriers-to-participation by including suffi-
cient metadata, so that researchers interested in a broad
range of applications, with a range of expertise, can
utilise the test collection.


Topic Title Total Relevant Recall (Automatic) Recall (Interactive)
The Red Taxi 1 1 1
Photographing a Lake 2 2 N/A
Presenting/Lecturing 3 2 0
Tower Bridge 1 1 1
Driving a Rental Car 19 0 5
Attending a Lecture 1 0 0
Eating while in Conversation 1 1 1
On the Bus or Train 4 2 1
New Key 1 0 1
Having a Drink 2 0 1
Lost 1 1 N/A
Riding a Red Train 3 2 3
Man in a Burberry Coat 1 1 N/A
The Church 1 1 1
The Rugby Match 3 1 3
Costa Coffee 4 2 2
Antiques Store 3 3 3
Outdoor Computing 2 0 1
Building a Computer 14 1 1
Airbus A380 1 1 1
Shopping for a Bottle of Wine 1 1 N/A
ATM 3 3 1
Shopping For Fish 3 2 3
Repairing a Car Wheel 1 0 1
Cycling home 7 0 5
Happy Homework 1 0 N/A
Shopping 14 9 5
Informal Coffee Meeting 1 1 N/A
Lunchtime 8 4 0
In a Meeting 8 4 1
Bus to the Airport 1 1 1
A Movie on the Flight 1 1 0
The Metro 12 9 3
A Garden Chat with Dog 1 1 1
Lion at the Gate 1 1 1
Checking the Menu 3 0 0
The BirdaˆA˘Z´s Nest Stadium 3 2 1
Watching TV 21 0 2
Grocery Store 39 11 18
Strolling on the Deck 1 0 N/A
Eating on the Roadside 1 0 1
Writing 51 4 1
The Elevator 19 7 7
Car Repair 1 0 1
Drinking in a Pub 18 6 11
Barbershop 1 1 N/A
Lottery 1 1 N/A
Checkout 30 2 4
Table 2: Statistical Analysis of NTCIR-12 Lifelog Data
descriptions and sentiment analysis to identify negative fea-
ture keywords. A final submission utilised query expansion
on every keyword in an attempt to enhance retrieval perfor-
mance.
LIG-MRM, France. The LIG-MRM group (LIG-MIRM in
Figure 5 and Automatic in Figure 6) focused on enhancing
the performance of the visual concept detectors to be used
for retrieval, and not relying on the CAFFE classifier out-
put [12]. This was achieved using three techniques, includ-
ing Dynamic Convolutional Neural Networks VGG (1000
dimensional features of ImageNet), a classification provided
by their own MSVM on optimized data TRECVid (346 di-
mensional features), and a third approach utilising MSVM
classification on VOC concepts (20 dimensional features).
The images were also described by their respective anno-
tated metadata (when present), such as place (e.g. ”home”,
”work”, etc.) and activity (e.g. ”walking”, ”transport”, ”bus”,
etc.). When processing a topic Q, a mapping (string inclu-
sion) of each word from Q into one or more visual concepts
was performed. The score of each image was then computed

Time Elapsed Num. of Relevant Moments Found Number of Topics for which a Relevant Mo-
ment was Found
10s 7 3
30s 20 9
60s 32 15
120s 56 27
300s 94 34
Table 3: Interactive Run Comparison over Time
initial step towards this goal, the NTCIR Lifelog data was
manually analysed from the viewpoint of individual sleeping
habits and in our paper we discuss possible approaches to
leveraging such data for the next version of Sleep-flower.
Toyohashi University, Japan. The group from Toyohashi
examined repeated pattern discovery from lifelog image se-
quences, by applying a Spoken Term Discovery technique,
which is an approach usually used to words from speech
data [16]. A variant of Dynamic Time Warping was used
in an experimental approach to extract extract meaningful
patterns from the lifelog data. It is suggested that this could
be a useful approach for insight generation from archives of
lifelog data.
Dublin City University, Ireland. The submission from
Dublin City University introduced an interactive lifelog in-
terrogation system which allowed for manual interrogation
of the lifelog dataset for the occurrence of visual concepts
that were assumed to match the information needs [3]. The
results of this manual interrogation were then used to gen-
erate insights and infographics for the provided topics.
5. LEARNINGS / FUTURE PLANS
This was the first collaborative benchmarking exercise for
lifelog data. It attracted eight participants, four for the
automatic LSAT search task, one for the interactive LSAT
search task and three for the LIT task. We can summarise
the learnings from this pilot task as follows:
• There is still no standardised approach to retrieval of
lifelog data; each of the participants in the LSAT task
took different approaches to retrieval. This suggests
that the LSAT task is valuable to run again in future
years.
• The dataset should contain more semantically rich data
to support mare groups to take part. Such data should
try to capture the semantics of daily life, and not just
the activities.
• The supplied metadata and visual concepts should be
at a higher-level of quality. The positive effect of higher-
quality metadata can be seen in the results of the best-
performing automatic LSAT run.
• The LSAT task is a valuable task, though effort should
be made to encourage more interactive participants.
• The LSAT unit-of-retrieval being the moment/event
required the mapping of submitted image IDs to mo-
ments that are defined by the coordinators in a manual
process. This suggests that the unit of retrieval itself
could become an interesting task, so we propose to in-
clude an event-segmentation task in the next running
of the lifelog collaborative benchmarking exercise.
6. CONCLUSION
In this paper, we described the data and the activities
from the first lifelog pilot-task at NTCIR. There were two
sub-tasks, the LSAT known-item search task and the LIT
data insights task. These task were undertaken by five
and three participants respectively. The results attained
by the participants showed that although many different
approaches for automatic retrieval were applied, the one
that appeared to work best was a computer-vision-based
approach that augmented the provided CAFFE concept de-
tector output with an enhanced concept detector based on
a CNN-based model. In terms of interactive vs. automatic
search, the interactive system performed better, as would
be expected. The LSAT task is a valuable task and should
continue, though perhaps joined by additional tasks, such as
event segmentation. It is proposed that a bigger and more
semantically rich dataset be employed for any future lifelog
comparative benchmarking tasks.
Acknowledgements
We acknowledge the financial support of Science Founda-
tion Ireland (SFI) under grant number SFI/12/RC/2289
and the input of the DCU ethics committee and the risk
& compliance officer. We acknowledge financial support by
the European Science Foundation via its Research Network
Programme “Evaluating Information Access Systems”. The
project has been reviewed and approved by the Research
Ethics Committee of the College of Arts of University of
Glasgow.
7. REFERENCES
[1] A. Cavoukian. Privacy by design: The 7 foundational
principles. implementation and mapping of fair
information practices. Information and Privacy
Commissioner of Ontario, Canada, 2010.
[2] G. de Oliveira Barra, A. C. Ayala, M. BolaA˜s´os,
M. Dimiccoli, M. Aghaei, M. CarnA˜l’, X. Giro-I-Nieto,
and P. Radeva. Lemore: A lifelog engine for moments
retrieval at the ntcir-lifelog lsat task. In Proceedings of
NTCIR-12, Tokyo, Japan, 2016.
[3] A. Duane, C. Gurrin, L. Zhou, and R. Gupta. Visual
insights from personal lifelogs - insight at the ntcir-12
lifelog lit task. In Proceedings of NTCIR-12, Tokyo,
Japan, 2016.
[4] C. Gurrin, R. Albatal, H. Joho, and K. Ishii. A
privacy by design approach to lifelogging. In Digital
Enlightenment Yearbook 2014, pages 49–73. IOS Press,
2014.
[5] C. Gurrin, A. F. Smeaton, and A. R. Doherty.
Lifelogging: Personal big data. Foundations and
Trends in Information Retrieval, 8(1):1–125, 2014.
[6] S. Hodges, L. Williams, E. Berry, S. Izadi,
J. Srinivasan, A. Butler, G. Smyth, N. Kapur, and
K. R. Wood. Sensecam: A retrospective memory aid.
In UbiComp 2006, pages 177–193, 2006.
[7] S. Iijima and T. Sakai. Slll at the ntcir-12 lifelog task:
Sleepflower and the lit subtask. In Proceedings of
NTCIR-12, Tokyo, Japan, 2016.
[8] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev,
J. Long, R. Girshick, S. Guadarrama, and T. Darrell.
Caffe: Convolutional architecture for fast feature
embedding. In ACM MM, pages 675–678, 2014.
[9] G. J. Jones, C. Gurrin, L. Kelly, D. Byrne, and
Y. Chen. Information access tasks and evaluation for
personal lifelogs. 2008.
[10] D. Kahneman, A. B. Krueger, D. A. Schkade,
N. Schwarz, and A. A. Stone. A survey method for
characterizing daily life experience: The day
reconstruction method. Science, 306:1776–1780, 2004.
[11] H. L. Lin, T. C. Chiang, L.-P. Chen, and P.-C. Yang.
Image searching by events with deep learning for
ntcir-12 lifelog. In Proceedings of NTCIR-12, Tokyo,
Japan, 2016.
[12] B. Safadi, P. Mulhem, G. Qul’not, and J.-P. Chevallet.
Mrim-lig at ntcir lifelog semantic access task. In
Proceedings of NTCIR-12, Tokyo, Japan, 2016.
[13] H. Scells, G. Zuccon, and K. Kitto. Qut at the ntcir
lifelog semantic access task. In Proceedings of
NTCIR-12, Tokyo, Japan, 2016.
[14] A. J. Sellen and S. Whittaker. Beyond total capture:
A constructive critique of lifelogging. Commun. ACM,
53(5):70–77, May 2010.
[15] L. Xia, Y. Ma, and W. Fan. Vtir at the ntcir-12 2016
lifelog semantic access task. In Proceedings of
NTCIR-12, Tokyo, Japan, 2016.
[16] K. Yamauchi and T. Akiba. Repeated event discovery
from image sequences by using segmental dynamic
time warping: experiment at the ntcir-12 lifelog task.
In Proceedings of NTCIR-12, Tokyo, Japan, 2016.


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