The%20Bayesian%20Image%20Retrieval%20System,PicHunter - PowerPoint PPT Presentation

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The%20Bayesian%20Image%20Retrieval%20System,PicHunter

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With an explicit model of a user's actions, assuming a desired goal, PicHunter ... HSV-HIST. Hue, Saturation, Value histogram. HSV-CORR. RGB-CCV. Color histogram ... – PowerPoint PPT presentation

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Title: The%20Bayesian%20Image%20Retrieval%20System,PicHunter


1
The Bayesian Image Retrieval System,PicHunter
  • Theory, Implementation, and Psychophysical
    Experiments

2
Introduction
  • Relevance feedback
  • users give additional information
  • Main idea
  • With an explicit model of a users actions,
    assuming a desired goal, PicHunter uses Bayes
    rule to predict the goal image, given their
    actions

3
Nature of search
  • Target-specific search (Target search)
  • exact match
  • Category search
  • same category is ok
  • Open-ended search (browsing)

4
Bayes formula
  • Fj hypothesis (Target image is j)
  • E experiment (users response behavior)
  • Show us how the correctness of a hypothesis
    change after carrying out an experiment
  • How to model P(EFj)?

5
Theoretical basis for PicHunter
  • During each session
  • a set Dt of ND images, Action At
  • H t ----History of the session

6
User ModelAssessing Image similarity
  • Key term
  • P(AtTTi,Dt,U)
  • Uspecific user
  • Purposeupdate the probability of each Ti being
    target

7
Relevance feedback
  • e.g. 2AFC (two-alternative forced-choice)
  • Given two image, user need to choose which one is
    similar to target
  • P(EFj) ? P(A1X1,X2,TTi)
  • 1 if d(X1,Ti) lt d(X2,Ti)
  • 0.5 if d(X1,Ti) d(X2,Ti)
  • 0 d(X1,Ti) gt d(X2,Ti)
  • Another one is relative distance

8
Relative distance measure
  • using the pictorial features distance
  • as the form of the probability
  • When ND2, At1 or 2
  • Psigmoid(A1X1,X2,T)

9
Pictorial features
  • HSV-HIST
  • Hue, Saturation, Value histogram
  • HSV-CORR
  • RGB-CCV
  • Color histogram

10
Display Updating Model
  • Most-Probable Display Updating Model
  • Give the most similar one for user to choose
  • Most-informative Display Updating Model
  • CP(T)
  • Give both similar and dissimilar images for use
    to choose

11
Results
  • Cox formulated an experiment XYZ
  • X - with memory or with out
  • Use all the response or just response in one
    iteration
  • Y - with using relative / absolute distance
    measure
  • Z use pictorial or semantic measure
  • Benchmark - how many images need to be displayed
    before target is found
  • MRS is the best
  • With memory, use relative distance and semantic
    measure
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