Browsing Personal Images Using Episodic Memory Chufeng Chen School of Computing and Technology, University of Sunderland

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

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

Time and location Clustering model

Example of Data sets, and how to separate events

Example of User interface

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)

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

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 , p = Average searching time specific scenario tasks , p = Average total finish time , p =

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 , p= This browser is easy to use , p= This browser feels familiar , p= It is easy to find the photo I am looking for , p< A month from now, I would still be able to find these photos , p= I was satisfied with how the pictures were organized , p< Total , p<

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).

R & P Results Time and location clustering.Time Alone clustering RecallPrecisi- on F 1 measure RecallPrecisi- on F 1 measure Subject Subject Subject Subject Subject Subject Subject Subject Subject Subject Average

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

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

Location Annotation Data

Search Engine Example