This document describes a study on a real-time guidance camera interface to enhance photo aesthetic quality for novice photographers. The system uses computer vision techniques to detect the region of interest in a photo, assess how well it follows the rule of thirds composition guideline, and provides real-time feedback to users. A user study found that photos taken with the guidance interface were rated significantly higher in aesthetic quality by experts and workers than a static gridline interface and better conformed to the rule of thirds. The interface was effective in improving photo composition and quality for novice users.

1. Real-time Guidance Camera Interface to
Enhance Photo Aesthetic Quality
Yan Xu1, Joshua Ratcliff1, James Scovell2, Gheric Speiginer3, Ronald
Azuma1
1Intel Labs 2Intel Corporation 3Georgia Institute of Technology

2. Motivation
2http://illuminatedmoments.com/blog/clarification-why-i-hired-a-professional/

3. Learning Photography Takes Time
3

4. Real-time Guidance for Novice Users
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A Camera Interface that can
• Understand the scene
• Know the object of interest
• Give concrete guidance
Research question:
Is the real-time guidance interface an effective way to enhance
photos’ aesthetic quality?

5. Choosing One Photography Rule in One Photo
Scenario
• Rule-of-thirds
• 1) important compositional elements should be placed along these
lines or their intersection [1]
• 2) the proportion of the object of interest should be roughly one third of
the total image space [2]
• One person portraiture
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[1] Peterson, B. F. (2003). Learning to see creatively, Amphoto Press.
[2] Smith, J. T. (1797). Remarks on rural scenery. Nathaniel Smith ancient Print. Cited by the Wikipedia
page about rule of thirds: http://en.wikipedia.org/wiki/Rule_of_thirds (retrieved September 10, 2014)

6. An Example for Rule-of-thirds
7

7. System: Three components

8. System Components
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1. Find the region of interest by face detection and foreground
segmentation
+

9. System Components
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2. Calculate how much does the region of interest follow rule-of-thirds
The bitmap mask for calculating the alignment between
subject-of-interest and rule-of-thirds

10. System Components
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3. User interface that guides users to move the camera in the space

11. User Study

12. Procedure
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• 40 users take portraiture photos of their friend, using our interface
and a static grid interface
• 24 professional photographers rated the photos
• 48 Mechanical Turk raters rated the photos

13. Interface for Raters
14

14. Quantitative Results (1)
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• Photos taken with real-time guidance UI has better aesthetic quality than static gridline
UI
• Using the two-factor repeated measures ANOVA, we found that expert photographers and
Mechanical Turk workers (MT) rated the photos taken by real-time guidance interface to be
significantly better than those taken by static gridline interface (expert: F = 7.62, p < .05, η2
partial =
.249); MT: F = 20.41, p < .01, η2
partial = .303).
Raters
Real-time Guidance
UI
Static Gridline UI
Expert
photographers
M = 43.95
SD = 22.99
M = 40.60
SD = 22.08
Mechanical Turk
workers
M = 65.98
SD = 21.87
M = 60.92
SD = 22.42

15. Quantitative Results (2)
16
• Users follow rule-of-thirds better when they use real-time guidance
interface
• Users align the subject to the rule-of-thirds grid better with the RG interface
than the SG interface (average diff = 31.76 (on a 0-250 scale), p < .05, one
tailed paired t-test)
• The proportion of human subject’s width is significantly closer to 1/3 when
using the RG interface compared to the SG interface (average difference =
6%, p < .01, one tailed paired t-test). Users tend to have smaller
subject/image ratios when using the SG interface

16. Qualitative Findings - Experts
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1. FACE: Genuine/natural smile, emotion, eye
contact, glass reflection, teeth, skin tone,
2. BODY: Aliveness, Natural poses, hands,
sense of movement and action, head/body
proportion
4. BACKGROUND: leading lines (and other
prominent lines) and vanishing points,
distraction, complement the subject or not.
3. AROUND THE SUBJECT: subject saliency,
distracting background right next to the subject
5. BIG PICTURE: Composition (balance, camera
angle/distance , rule of thirds), Lighting (exposure,
evenness, shadows), Color (white balance,
saturation, Camera angle
50%10%0% 20% 30% 40%

17. Qualitative Findings – Mechanical Turk Raters
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1. FACE: Smile, facial expression (naturalness,
confidence, attractiveness), teeth, skin, hair,
mood(more fun, happier, more relaxed), eye
contact
2. BODY: Natural poses, ore flattering body
shape; less distraction or cut off by other
elements; pose, clothes
5. BIG PICTURE: Lighting and shadows, Color
(tone, accuracy, saturation, glare, patches,
vividness, true to reality, harmony), composition,
camera angle and distance, sharpness
4. BACKGROUND: less distraction, realistic or
not, broader view and context, leading lines
3. AROUND THE SUBJECT: subject saliency,
distracting background right next to the
subject, fg/bg harmony
50%10%0% 20% 30% 40%

18. Conclusion and Future Work

19. Take-away Message
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• Real-time guidance interface is effective in terms of improving photo
aesthetic quality and user’s conformation to photography rules
• Understanding photos in both RGB and depth can help us better
evaluate photo quality and provide feedback

20. New Capabilities of Understanding Photos
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• Depth
• Segmentation
• Geometry
• Lighting
…

21. Thank you!
Real-time Guidance Camera Interface to
Enhance Photo Aesthetic Quality
Yan Xu, Joshua Ratcliff, James Scovell, Gheric Speiginer , Ronald Azuma
contact: yan.xu@intel.com