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Relevance Feedback based on Parameter Estimation of Target Distribution

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### Relevance Feedback based on Parameter Estimation of Target Distribution

### END

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)
- Former approaches
- Tackling the problem
- Parameter estimation of target distribution
- Experiments
- Future works and conclusion

Relevance Feedback Based on Parameter Estimation of Target Distribution

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

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

Display

UserFeedback

Feedbackto system

Estimation &

Display selection

2nd iteration

Display

UserFeedback

Relevance Feedback Based on Parameter Estimation of Target Distribution

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

Comparison

Relevance Feedback Based on Parameter Estimation of Target Distribution

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

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

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

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

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

Updating Parameters

- Estimated mean is the average
- Estimated variance by differentiation
- Iterative approach

Relevance Feedback Based on Parameter Estimation of Target Distribution

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

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

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

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

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

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

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

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

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