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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
  • 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
  • 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

G

B

Feature Extraction Model

Relevance Feedback Based on Parameter Estimation of Target Distribution

relevance feedback
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

slide6

1st 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
  • 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

Relevance Feedback Based on Parameter Estimation of Target Distribution

the model
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
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
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

estimating target distribution

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

expectation maximization
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
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
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

maximum entropy display1

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

experiment
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
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
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
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
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

slide33

END

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

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