Homework 9 17-2011

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Homework 9 17-2011

  1. 1. CrowdSearch: Exploiting Crowds for Accurate Real-time Image Search on Mobile Phones<br />TienShengWen<br />Department of Electronic Engineering<br />Chung-Yuan Christian University, Taiwan<br />Tingxin Yan, Vikas Kumar, Deepak Ganesan<br />Department of Computer Science<br />University of Massachusetts, Amherst, MA 01003<br />{yan, vikas, dganesan}@cs.umass.edu<br />
  2. 2. Outline<br /><ul><li>Introduction
  3. 3. System Architecture
  4. 4. Crowdsearch for Search
  5. 5. CrowdsearchAlgorithm
  6. 6. Image Search Engine
  7. 7. System Implementation
  8. 8. Experimental Evaluation
  9. 9. Conclusions</li></li></ul><li> Introduction<br /><ul><li> Image search system for mobile phones
  10. 10. Real-time validation
  11. 11. Beyond Image Search
  12. 12. System Performance
  13. 13. Payment</li></li></ul><li> System Architecture<br />CrowdSearchis implemented on <br />Apple iPhone and Linux servers.<br />Requires three pieces of information prior to initiating search: <br />(a) A image query<br />(b) A query deadline<br />(c) A payment mechanism <br /> for human validators<br />
  14. 14. Crowdsearchfor Search<br /><ul><li>Amazon Mechanical Turk (AMT)
  15. 15. Constructing Validation Tasks
  16. 16. Minimizing Human Bias and Error
  17. 17. Pricing Validation Tasks</li></li></ul><li>Crowd Search Algorithm (1/2)<br /><ul><li>Optimizing Delay and Cost</li></li></ul><li>Crowd Search Algorithm (2/2)<br /><ul><li>Delay Prediction Model</li></ul>Case 1 - Delay for the first response:<br /> Case 2 - Inter-arrival delay between responses:<br />
  18. 18. Image Search Engine (1/2)<br /><ul><li>The image search process contains two </li></ul> major steps:<br /> (1) Extracting features from a query image good features: <br />Scale-Invariant Feature Transform (SIFT)<br /> (2) Search through database images with features of <br /> query image.<br />
  19. 19. Image Search Engine (2/2)<br />A SeqTree to Predict Validation Results.<br />The received sequence is ‘YNY’, the two sequences<br />that lead to positive results are ‘YNYNY’<br />and ‘YNYY’. The probability that ‘YNYY’ occurs<br />given receiving ‘YNY’ is 0.16/0.25 = 64%<br />
  20. 20. System Implementation<br />CrowdSearch Implementation Components Diagram<br />
  21. 21. Experimental Evaluation (1/3)<br /><ul><li> Datasets
  22. 22. Improving Search Precision
  23. 23. Accuracy of Delay Models</li></li></ul><li>Experimental Evaluation (2/3)<br /><ul><li>CrowdSearchPerformance</li></li></ul><li>Experimental Evaluation (3/3)<br /><ul><li> Varying user-specified deadline</li></li></ul><li>Conclusions<br /><ul><li>Multimedia search presents a unique challenge.</li></ul> Because image search system is still far from reality.<br /><ul><li>Humans are excellent at distinguishing images, thus human validation can greatly improve the precision of image search. However, human validation costs time and money, hence we need to dynamically optimize these parameters to design an real-time and cost-effective system.</li>

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