Reading user's mind from there eyes... The eyes are tracked with help of a tracker and then based on the eye movement the pages are been opened .

1. )
PRESENTED BY:
ARSHA RAMAN
ROLL NO: 7226
CS-7
GUIDE :Ms.RESHMI .S
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2. Content
• INTRODUCTION
• WHAT IS ANNOTATION ?
• TYPES OF ANNOTATION
• 3 APPROACHES FOR IMAGE ANNOTATION
• EYE TRACKING
• FRAMEWORK
• FEATURE EXTRACTION UNIT
• MODEL CONSTRUCTION UNIT
• FUTURE SCOPE
• CONCLUSION
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3. INTRODUCTION
• Function of vision is to provide information
needed to support action
• Humans use their selective visual attention
inorder to interact with surrounding
• Volitional shift of attention
• Automatic capture of attention
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4. What is image
annotation?
• Database of image is
explored & images are
tagged according to visual
contents
• Automatic image
tagging(Adding metadata to
it )
Reason to annotate :
• More effective searches
• Text search technique
Problem:
• Extremely labour-intensive
• Time consuming task
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5. Types of annotation
• Explicit annotation:
explicitly annotate our image with
caption
• Implicit annotation:
Here we imbed image into text
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6. 3 approaches for image
annotation
1) Manual – By human
Advantages:
 Accurate
Disadvantage :
 Time consuming
 Expensive
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7. 2) Automatic-By computer
Advantages:
 Cheap
 Prompt
 Fast
Disadvantages:
 Inaccuracy
 Occurrence of semantic gap
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8. 3) Semi-automatic-By human
& computer
Advantages:
 Accurate as Human brain
 Cheaper than Manual annotation
 Faster than Manual annotation
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9. EYE TRACKING
• Fixation and saccade are the important eye
movement for the process of revealing user’s
interest .
• Period of time when eyes are still –Fixation
• Fast eye movements – Saccade
• Equipments
 60Hz cameras with Infra-red filters
FaceLAB 5.0 software package as eye tracking
technology
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10. Fig: A snapshot of video image of eye tracker
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11. FRAMEWORK
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12. Framework Scenario Snapshots
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a)START PAGE (TC)
b)SCENARIO

13. FEATURE EXTRACTION UNIT
• INPUT- 3 vectors of fixation
Fixation Duration
ID
Spatial properties of the image on screen
• OUTPUT – 2 feature vector(FV)
 Image Feature Vector(IFV)
Transition Feature Vector(TFV)
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15. MODEL CONSTRUCTION UNIT
• Structure based on FUZZY LOGIC
• INPUT
 Consist of Extracted FV for every image and
corresponding transition they belong to
• OUTPUT
 It may be +1 or -1 if image belongs to TC
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16. FIS(FUZZY INFERENCE SYSTEM )
• System that uses Fuzzy Logic based algorithm
are called FIS
• Behaviour of FIS is governed by IF THEN rule
 Example : If temperature is high and stored food
amount is large, then fan speed is very high
• Rule divided into 2 parts
 Premise
Consequent
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17. FUTURE SCOPE
• Implicit image annotation by playing games
– Tag Captcha
• Monitoring brain waves by EEG
• Driving vehicles -system that tracks a driver's
eye movements and issues a warning before
the driver has an opportunity to nod off to
sleep.
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18. CONCLUSION
• Real-Time user adaptive Framework is
Introduced which is capable of measuring the
Interest of user’s to images that appear on
screen by tracking their eye’s
• Framework is flexible and has Higher accuracy
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19. REFERENCES
• F. Marquez, A. Peregrin, and F. Herrera,
“Cooperative evolutionary learning of linguistic
fuzzy rules and parametric aggregation
connectors for mamdani fuzzy systems,” IEEE
Trans.
• H. Ma, J. Zhu, M.-T. Lyu, and I. King, “Bridging
the semantic gap between image contents and
tags,” IEEE Trans
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