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

2. 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

3. Development of Time & Location Clustering Model   Time and location Clustering model   Example of Data sets, and how to separate events   User interface

4. Time and location Clustering model

5. Example of Data sets, and how to separate events

6. Example of User interface

7. User 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)

8. Experimental 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

9. Experiment Results (scenario tasks searching time) Time & location combine d BR's Photo- Archiver Canon Zoom- Browser- EX Un- indexed browser Time alone ANOVA F(4, 45) = 1. Average searching time general scenario tasks 53.9148.410192.779.7 3.61, p = 0.0123 2. Average searching time specific scenario tasks 39.286.479.278.453.6 4.08, p = 0.0066 3. Average total finish time 93.1234.8180.2171.1133.3 4.78, p = 0.0027

10. Experiment Results (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 433.32.73.8 6.048, p= 0.0006 2. This browser is easy to use 4.33.13.73.14 4.98, p=0.0020 3. This browser feels familiar 42.83.63.43.5 3.14, p= 0.023 4. It is easy to find the photo I am looking for 4.32.93.22.23.8 10.63, p< 0.0001 5. A month from now, I would still be able to find these photos 4.23.23.73.24.1 3.67, p= 0.011 6. I was satisfied with how the pictures were organized 4.32.93.12.23.8 9.59, p< 0.0002 Total 25.117.920.616.823 12.26, p< 0.0001

11. System Centre Evaluation  Recall and Precision 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 places them in different events; 3. user places the image pair in different events but the machine places them in the same event; 4 user and machine both place the image pair into separate events. Recall = (pairs in 1) / (pairs in 1 + pairs in 2) Precision = (pairs in 1) / (pairs in 1 + pairs in 3).

12. R & P Results Time and location clustering.Time Alone clustering RecallPrecisi- on F 1 measure RecallPrecisi- on F 1 measure Subject10.72891.00000.84320.94190.69650.8008 Subject20.99270.96470.97850.69030.60710.6551 Subject30.79560.92900.85710.99620.27570.4319 Subject40.88260.94490.91270.88320.94220.8976 Subject50.84350.97470.90440.99790.35550.5242 Subject60.88470.99560.93690.88470.99560.9369 Subject70.92210.99570.95750.87410.66840.7576 Subject80.76330.99440.86370.76330.96750.8534 Subject90.83311.00000.90900.83310.81500.8240 Subject100.90751.00000.95150.92900.98380.9556 Average0.85540.97990.91150.87940.73070.7637

13. Findings  Time and location browser significantly better than other four standard browsers in both searching time and user satisfaction  Time and location combination browser had greater retrieval effectiveness than the time alone browser  Factors related to human episodic memory, time and location, can be used to help users search their personal photograph collections more easily

14. Works So Far  Develop a Location Annotation System for Personal Images (annotating by location gazetteer)  Develop a Keyword Search Engine of System Annotation and User Annotation  Evaluation User study: system annotation Vs. User Annotation Vs. T & L Browsing User study: system annotation Vs. User Annotation Vs. T & L Browsing Recall and Precision: System annotation Vs. User annotation Recall and Precision: System annotation Vs. User annotation

15. Location Annotation Data

16. Search Engine Example