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Relevance Feedback based on Parameter Estimation of Target Distribution

Relevance Feedback based on Parameter Estimation of Target Distribution. K. C. Sia and Irwin King Department of Computer Science & Engineering The Chinese University of Hong Kong 15 May IJCNN 2002. Agenda. Introduction to content based image retrieval (CBIR) and relevance feedback (RF)

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Relevance Feedback based on Parameter Estimation of Target Distribution

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  1. Relevance Feedback based on Parameter Estimation of Target Distribution K. C. Sia and Irwin King Department of Computer Science & Engineering The Chinese University of Hong Kong 15 May IJCNN 2002

  2. Agenda • Introduction to content based image retrieval (CBIR) and relevance feedback (RF) • Former approaches • Tackling the problem • Parameter estimation of target distribution • Experiments • Future works and conclusion Relevance Feedback Based on Parameter Estimation of Target Distribution

  3. Content Based Image Retrieval • How to represent an image? • Feature extraction • Colour histogram (RGB) • Co-occurrence matrix texture analysis • Shape representation • Feature vector • Map images to points in hyper-space • Similarity is based on distance measure Relevance Feedback Based on Parameter Estimation of Target Distribution

  4. R G B Feature Extraction Model Relevance Feedback Based on Parameter Estimation of Target Distribution

  5. Relevance Feedback • Relevance feedback • Architecture to capture user’s target of search • Learning process • Two steps • Feedback – how to learn from the user’s relevance feedback • Display – how to select the next set of documents and present to user Relevance Feedback Based on Parameter Estimation of Target Distribution

  6. 1st iteration Display UserFeedback Feedbackto system Estimation & Display selection 2nd iteration Display UserFeedback Relevance Feedback Based on Parameter Estimation of Target Distribution

  7. Former Approaches • Multimedia Analysis and Retrieval System (MARS) • Yong Rui et al. Relevance feedback: A powerful tool for interactive content-based image retrieval. - 1998 • Using weight to capture user’s preference • Pic-Hunter • Ingemar J. Cox et al. The Bayesian image retrieval system, pichunter, theory, implementation, and psychophysical experiments. - 2000 • Images are associated with a probability being the user’s target • Bayesian learning Relevance Feedback Based on Parameter Estimation of Target Distribution

  8. Comparison Relevance Feedback Based on Parameter Estimation of Target Distribution

  9. The Model • Feature Extraction • I - raw image data •  - set of feature extraction method • f - feature extraction operation • Images  data point in hyper-space (Rd) • Problem scope is narrowed down to a particular feature Relevance Feedback Based on Parameter Estimation of Target Distribution

  10. Feedback

  11. Inconsistence in Feedback • User tells lies • Too many false positive or false negative • Conflict of feedback in each iteration by careless mistake Relevance Feedback Based on Parameter Estimation of Target Distribution

  12. Resolving Conflicts • How to deal with inconsistent user feedback? • Maintain a relevance measure for each data points • Relevance measure > 0 counted as relevant and use in estimation Relevance Feedback Based on Parameter Estimation of Target Distribution

  13. Red Data points selected as relevant Estimating Target Distribution • User’s target is a cluster • Assume it follows a Gaussian distribution • Model a distribution that fits the relevant data points • Based on the parameterof distribution, systemlearns what user wants Relevance Feedback Based on Parameter Estimation of Target Distribution

  14. Expectation Maximization • Fitting a Gaussian distribution function using feedback data points • By expectation maximization • Distribution represent user’s target • Expectation function match the display model Relevance Feedback Based on Parameter Estimation of Target Distribution

  15. Updating Parameters • Estimated mean is the average • Estimated variance by differentiation • Iterative approach Relevance Feedback Based on Parameter Estimation of Target Distribution

  16. Display

  17. Maximum Entropy Display • Why maximum entropy display? • Reason: fully utilize information contained in user feedback to reduce number of feedback iteration • Result: near boundary images will be selected to fine tune parameters Relevance Feedback Based on Parameter Estimation of Target Distribution

  18. Querytargetclustercenter Selectedby knnsearch Selectedby Max.Entropy Maximum Entropy Display • How to simulate maximumentropy display in ourmodel? • Data points 1.18  away from  are selected • Why 1.18? • 2P(+1.18)=P() Relevance Feedback Based on Parameter Estimation of Target Distribution

  19. Experiment • Synthetic data generated by Matlab • Mixture of Gaussians • Class label of data points shown for reference to give feedback • Dose it works and works better? Relevance Feedback Based on Parameter Estimation of Target Distribution

  20. Convergence • Is the estimated parameter (mean and variance) converge to the actual parameter of target distribution? • Is the maximum entropy display correctly done? Relevance Feedback Based on Parameter Estimation of Target Distribution

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  24. Performance • Compares to Rui’s intra-weight updating model • Nearest neighbour search performed after several feedbacks (6-7 iterations) • Data points outside 2  are discarded in our algorithm • Precision-Recall graph Relevance Feedback Based on Parameter Estimation of Target Distribution

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  31. Future Works • Modification to learn from information contained in non-relevant set • To capture correlation in different features • Apply in CBIR system for performance measurement Relevance Feedback Based on Parameter Estimation of Target Distribution

  32. Conclusion • Proposed an approach to interpret the feedback information from user and learn his target of search • Compares our approach with Rui’s intra-weight updating method Relevance Feedback Based on Parameter Estimation of Target Distribution

  33. END Presentation file available athttp://www.cse.cuhk.edu.hk/~kcsia/research/

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