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CrowdSearch: Exploiting Crowds for Accurate Real-time Image Search on Mobile Phones
Original work by Yan, Kumar & GanesanPresented by Shibo Li & Jian Yu
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Problem Definition
• How to search information?
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Problem Definition
• Mobile-based search will become more important in the future.– More than 70% of smart phone users perform searches.
• Expected to be more mobile searches than non-mobile searches soon
– Text-based mobile searches are easy as well…
• What about searching images?
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Problem Definition
• Image search using mobile phones
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Problem Definition
• Automatic searching
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Idea
• Image searching based on crowd source.
CrowdSearch Algorithm
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Challenges
• Automatic image search: – Delay↓, Cost ↓, Accuracy ↓
• People validation image search:– Delay ↑, Cost ↑, Accuracy ↑
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CrowdSearch Algorithm
Overview
Implementation & Evaluations
Throughts & Criticisms
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CrowdSearch: Overview
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CrowdSearch: Overview
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CrowdSearch: Overview
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CrowdSearch: Overview
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CrowdSearch: Overview
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Challenge: Accuracy
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Challenge: Accuracy
• Human validation improves accuracy 2-5 times.
• Majority(5) can achieve the highest accuracy up to 95%
So we send each image to 5 people to get the majority feedback.
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Challenge: Delay & Cost tradeoff
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Challenge: Delay & Cost tradeoff
• Parallel Scheme
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Challenge: Delay & Cost tradeoff
• Serial Scheme
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CrowdSearch: compromised scheme
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CrowdSearch: compromised scheme
• Prediction requires delay and accuracy models
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Delay Model
• Statistically, both of the delays follow the exponential distribution.
• Overall delay distribution is the convolution of the acceptance and submission delay.
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Delay Prediction
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Accuracy Prediction
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Decision Engine
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Overview
Implementation & Evaluations
Throughts & Criticisms
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Implementation
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Power Consideration
• Should some image processing occur on the local device or should it be outsourced to the server?
– Use remoteprocessing when WiFi is available.
– Use local processingwhen only 3G is available
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Evaluation
• Delay model meets the exponential distribution
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CrowdSearch Performance
CrowdSearch optimized algorithm
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Overview
Implementation & Evaluations
Throughts & Criticisms
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Thoughts/Criticism
• Only 1000 images in the backend database.– Would increasing the number of automated search images increase
total task time in a significant way?
• The evaluation only based on 4 categories.– Buildings, Books, Flowers and Faces
• Suggestion:• Internet database• Let the user to choose the categories
• Too many distractions in a single image
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Thoughts/Criticism
• Too many disturbances in a single image
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Q&A
Thank you!