Browsing Personal Images Using Episodic Memory Chufeng Chen School of Computing and Technology,...
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![Page 1: Browsing Personal Images Using Episodic Memory Chufeng Chen School of Computing and Technology, University of Sunderland Email: Chufeng.chen@sunderland.ac.ukChufeng.chen@sunderland.ac.uk.](https://reader036.fdocuments.net/reader036/viewer/2022062516/56649d545503460f94a319a8/html5/thumbnails/1.jpg)
Browsing Personal Images Using Browsing Personal Images Using
Episodic MemoryEpisodic Memory
Chufeng Chen School of Computing and Technology, University of Sunderland
Email: [email protected]
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Related works
What is episodic memory Abrams et al. (1998) : Episodic memory in
HCI Platt et al. (2002) : Time clustering Naaman et al. (2004) : Time and Location
Classification Cooper et al. (2005) : Time and Colour
Clustering
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Development of Time & Location Development of Time & Location Clustering ModelClustering Model
Time and location Clustering model Example of Data sets, and how to
separate events User interface
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Time and location Clustering model
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Example of Data sets, and how to separate events
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Example of User interface
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User Centered EvaluationUser Centered Evaluation
The hypothesis: browsing features related to episodic memory, incorporated into our time and location combination browser would improve image searching of personal collections
10 Subjects (200 photo collections) Five Browsers
Time and location combination browser BR's Photo-Archiver Canon Zoom-Browser-EX Unindexed browser (WinXp image browser) Time alone (Platt, 2002)
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Experimental DesignExperimental Design
Latin-Square Design Scenario Searching Tasks
General Searching Tasks (4 for each subject) Specific Searching Tasks (4 for each subject)
Record Searching Time for each Scenario Tasks User Satisfaction Questionnaire for each System
Five Likert scale questionnaires The questionnaire had been used in Platt’s (2002) user
study
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Experiment Results (scenario Experiment Results (scenario tasks searching time)tasks searching time)
Time & location
combined
BR's Photo-
Archiver
Canon Zoom-
Browser-EX
Un-indexed browser
Time alone
ANOVA F(4, 45)
=
1. Average searching time general scenario tasks
53.9 148.4 101 92.7 79.73.61, p =
0.0123
2. Average searching time specific scenario tasks
39.2 86.4 79.2 78.4 53.64.08, p =
0.0066
3. Average total finish time
93.1 234.8 180.2 171.1 133.34.78, p =
0.0027
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Experiment Results Experiment Results (Questionnaire analysis)(Questionnaire analysis)
Time & location
combined
BR's Photo-Archiver
Canon Zoom-Browser-EX
Un-indexed browser
Time alone
ANOVA F(4, 45) =
1. I like this image browser 4 3 3.3 2.7 3.86.048, p= 0.0006
2. This browser is easy to use 4.3 3.1 3.7 3.1 44.98, p=0.0020
3. This browser feels familiar 4 2.8 3.6 3.4 3.53.14, p= 0.023
4. It is easy to find the photo I am looking for
4.3 2.9 3.2 2.2 3.810.63, p< 0.0001
5. A month from now, I would still be able to find these photos
4.2 3.2 3.7 3.2 4.13.67, p= 0.011
6. I was satisfied with how the pictures were organized
4.3 2.9 3.1 2.2 3.89.59, p< 0.0002
Total 25.1 17.9 20.6 16.8 2312.26, p< 0.0001
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System Centre Evaluation System Centre Evaluation
Recall and PrecisionRecall and Precision1. user and machine place the image pair in the same event; 1. user and machine place the image pair in the same event;
2. user places the image pair in the same event, but the machine 2. user places the image pair in the same event, but the machine places them in different events; places them in different events;
3. user places the image pair in different events but the machine 3. user places the image pair in different events but the machine places them in the same event; places them in the same event;
4 user and machine both place the image pair into separate 4 user and machine both place the image pair into separate
events.events. Recall = (pairs in 1) / (pairs in 1 + pairs in 2)Recall = (pairs in 1) / (pairs in 1 + pairs in 2)
Precision = (pairs in 1) / (pairs in 1 + pairs in 3).Precision = (pairs in 1) / (pairs in 1 + pairs in 3).
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R & P ResultsR & P ResultsTime and location clustering. Time Alone clustering
Recall Precisi-on
F1 measure Recall Precisi-on
F1 measure
Subject1 0.7289 1.0000 0.8432 0.9419 0.6965 0.8008
Subject2 0.9927 0.9647 0.9785 0.6903 0.6071 0.6551
Subject3 0.7956 0.9290 0.8571 0.9962 0.2757 0.4319
Subject4 0.8826 0.9449 0.9127 0.8832 0.9422 0.8976
Subject5 0.8435 0.9747 0.9044 0.9979 0.3555 0.5242
Subject6 0.8847 0.9956 0.9369 0.8847 0.9956 0.9369
Subject7 0.9221 0.9957 0.9575 0.8741 0.6684 0.7576
Subject8 0.7633 0.9944 0.8637 0.7633 0.9675 0.8534
Subject9 0.8331 1.0000 0.9090 0.8331 0.8150 0.8240
Subject10 0.9075 1.0000 0.9515 0.9290 0.9838 0.9556
Average 0.8554 0.9799 0.9115 0.8794 0.7307 0.7637
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FindingsFindings
Time and location browser significantly better Time and location browser significantly better than other four standard browsers in both than other four standard browsers in both searching time and user satisfaction searching time and user satisfaction
Time and location combination browser had Time and location combination browser had greater retrieval effectiveness than the time greater retrieval effectiveness than the time alone browser alone browser
Factors related to human episodic memory, time Factors related to human episodic memory, time and location, can be used to help users search and location, can be used to help users search their personal photograph collections more their personal photograph collections more easily easily
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Works So FarWorks So Far
Develop a Location Annotation System for Develop a Location Annotation System for Personal Images (annotating by location Personal Images (annotating by location gazetteer)gazetteer)
Develop a Keyword Search Engine of Develop a Keyword Search Engine of System Annotation and User AnnotationSystem Annotation and User Annotation
EvaluationEvaluation User study: system annotation Vs. User Annotation Vs. T & L User study: system annotation Vs. User Annotation Vs. T & L
BrowsingBrowsing Recall and Precision: System annotation Vs. User annotationRecall and Precision: System annotation Vs. User annotation
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Location Annotation Data Location Annotation Data
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Search Engine ExampleSearch Engine Example