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CrowdSearch : Exploiting Crowds for Accurate Real-time Image Search on Mobile Phones

CrowdSearch : Exploiting Crowds for Accurate Real-time Image Search on Mobile Phones. Original work by Yan, Kumar & Ganesan Presented by Shibo Li & Jian Yu. Problem Definition. How to search information?. Problem Definition. Mobile-based search will become more important in the future.

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CrowdSearch : Exploiting Crowds for Accurate Real-time Image Search on Mobile Phones

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  1. CrowdSearch: Exploiting Crowds for Accurate Real-time Image Search on Mobile Phones Original work by Yan, Kumar & Ganesan Presented by Shibo Li & Jian Yu

  2. Problem Definition • How to search information?

  3. 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?

  4. Problem Definition • Image search using mobile phones

  5. Problem Definition • Automatic searching

  6. Idea • Image searching based on crowd source. CrowdSearch Algorithm

  7. Challenges • Automatic image search: • Delay↓, Cost ↓, Accuracy ↓ • People validation image search: • Delay ↑, Cost ↑, Accuracy ↑

  8. CrowdSearch Algorithm

  9. CrowdSearch: Overview

  10. CrowdSearch: Overview

  11. CrowdSearch: Overview

  12. CrowdSearch: Overview

  13. CrowdSearch: Overview

  14. Challenge: Accuracy

  15. 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.

  16. Challenge: Delay & Cost tradeoff

  17. Challenge: Delay & Cost tradeoff • Parallel Scheme

  18. Challenge: Delay & Cost tradeoff • Serial Scheme

  19. CrowdSearch: compromised scheme

  20. CrowdSearch: compromised scheme • Prediction requires delay and accuracy models

  21. Delay Model • Statistically, both of the delays follow the exponential distribution. • Overall delay distribution is the convolution of the acceptance and submission delay.

  22. Delay Prediction

  23. Accuracy Prediction

  24. Decision Engine

  25. Implementation

  26. 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

  27. Evaluation • Delay model meets the exponential distribution

  28. CrowdSearch Performance • CrowdSearch optimized algorithm

  29. 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

  30. Thoughts/Criticism • Too many disturbances in a single image

  31. Q&A Thank you!

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