JCDL 2005 – June 8 th, 2005 User Perceptions of Digital Image Similarity Unmil Karadkar, Richard...
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JCDL 2005 – June 8th, 2005
User Perceptions of User Perceptions of Digital Image SimilarityDigital Image Similarity
Unmil Karadkar, Richard Furuta, Jeevan Joseph John
Center for the Study of Digital Libraries
Texas A&M University
Jin-Cheon Na
Division of Information Studies
Nanyang Technological University
JCDL 2005 – June 8th, 2005
OutlineOutline
• Motivation
• Study – Research questions– Setting and Design– Analysis of results
• Significance– Theoretical– Practical
JCDL 2005 – June 8th, 2005
MIDAS MIDAS (Multi-device Integrated Dynamic Activity Spaces)(Multi-device Integrated Dynamic Activity Spaces)
Multi-device Integrated Dynamic Activity Spaces
World-WideWeb
P E R C E P T I O N
JCDL 2005 – June 8th, 2005
Context of Information SpaceContext of Information Space
• Images– Graphs (visual data representations)– Line drawings (cartoons, comic strips)– Art (sketches, paintings)– Photographs (scanned or digital)
• Quick growth– Digital cameras– Camera phones
• Text • Audio
• Video • Animation
JCDL 2005 – June 8th, 2005
Research QuestionResearch Question
• How does the variation of image attributes affect user perception of images?
Variables:• Size (scaling)• Colors (change of color depth)
– How does the perception of similarity change?– Do users allow automatic substitution of
modified images for best display on a device?– How does variation affect the confidence of
image substitution?
JCDL 2005 – June 8th, 2005
Image CharacteristicsImage Characteristics
• Size (pixels)– 160x120 (smart phone)– 320x240 (PDA)– 640x480 (CSISD schools)– 800x600 (notebooks)– 1024x768 (desktop computers)
• Colors– 2 (b/w) (1-bit)– 4 (2-bit)– 16 (4-bit)– 256 (8-bit)– 16 Million (24-bit)
• Image types– People – Nature
– Structures – Text
• 4 images of each type
JCDL 2005 – June 8th, 2005
Subject Pool CharacteristicsSubject Pool Characteristics
• 5 Users
• Graduate students and staff
• Male and female
• Different academic backgrounds– Engineering, sciences, architecture
• Different nationalities and cultures
• Mid-twenties to early forties
JCDL 2005 – June 8th, 2005
Setting of the StudySetting of the Study
• Computer with 2 identical displays
• One image on each display
• Time not a factor
• Users could take a break
• Users could clarify doubts and ask questions
• Evaluator did not interrupt the users during the task
JCDL 2005 – June 8th, 2005
Design of StudyDesign of Study
• Users viewed pairs of images
• 3 questions per pair– Perceived level of similarity (9 pt. Likert scale)– Would they allow substitution of one image for
another (9 pt. Likert scale)– Automatic substitution acceptable (Yes/No)
• No confounding effects– Each pair differed in size or colors but not both
• 20 pairs for each variable
JCDL 2005 – June 8th, 2005
Results – Results – Perception of SimilarityPerception of Similarity
• Scales for size and color are not comparable
Steps Within color
Color to grayscale
0 - 6.89
1 6.69 5.57
2 4.6 4.55
3 5.86 4.00
4 4.14 -
Steps Similarity
0 -
1 7.12
2 7.11
3 6.87
4 6.14
Size Color
JCDL 2005 – June 8th, 2005
Results – Similarity by image type Results – Similarity by image type
• People’s faces• Legibility of textual elements• Nature and structural images scale well
Steps nature people structures text
1 6.57 7.17 7.67 6.78
2 7.33 7.09 6.78 7.25
3 7.75 7.00 7.00 6.29
4 7.00 5.71 7.50 5.00
Size
JCDL 2005 – June 8th, 2005
Results – Image Substitution Results – Image Substitution
• Automatic substitution– High confidence but fewer instances
• Substitution with warning– More instances but confidence dropped
Steps Automatic substitution
Confidence Substitution with warning
Confidence
1 44% 7.66 56% 6.05
2 49% 7.42 51% 6.00
3 50% 7.13 50% 6.50
4 21% 7.47 79% 4.18
Size
JCDL 2005 – June 8th, 2005
Significance of ResultsSignificance of Results• Theoretical
– Provides an insight into human image perception
– Nature and structural images scale well– Images of people and text scale to a lesser
degree
• Practical– Design guidelines for image optimization
• Scale images rather than reducing colors
– Use other techniques, such as cropping for better scaling of textual and people images