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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

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
Agenda Distribution

  • 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
Content Based Image Retrieval Distribution

  • 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


Feature extraction model

R Distribution

G

B

Feature Extraction Model

Relevance Feedback Based on Parameter Estimation of Target Distribution


Relevance feedback
Relevance Feedback Distribution

  • 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


1 Distributionst iteration

Display

UserFeedback

Feedbackto system

Estimation &

Display selection

2nd iteration

Display

UserFeedback

Relevance Feedback Based on Parameter Estimation of Target Distribution


Former approaches
Former Approaches Distribution

  • 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
Comparison Distribution

Relevance Feedback Based on Parameter Estimation of Target Distribution


The model
The Model Distribution

  • 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


Feedback

Feedback Distribution


Inconsistence in feedback
Inconsistence in Feedback Distribution

  • 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
Resolving Conflicts Distribution

  • 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


Estimating target distribution

Red 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
Expectation Maximization Distribution

  • 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
Updating Parameters Distribution

  • Estimated mean is the average

  • Estimated variance by differentiation

  • Iterative approach

Relevance Feedback Based on Parameter Estimation of Target Distribution


Display

Display Distribution


Maximum entropy display
Maximum Entropy Display Distribution

  • 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


Maximum entropy display1

Query Distributiontargetclustercenter

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
Experiment Distribution

  • 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
Convergence Distribution

  • 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
Performance Distribution

  • 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
Future Works Distribution

  • 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
Conclusion Distribution

  • 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


END Distribution

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


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