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The Bayesian Image Retrieval System,PicHunter

The Bayesian Image Retrieval System,PicHunter. Theory, Implementation, and Psychophysical Experiments. Introduction. Relevance feedback —— users give additional information Main idea:

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The Bayesian Image Retrieval System,PicHunter

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  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 user’s 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 (user’s response behavior) • Show us how the correctness of a hypothesis change after carrying out an experiment • How to model P(E|Fj)?

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

  6. User Model:Assessing Image similarity • Key term: P(At|T=Ti,Dt,U) U:specific user • Purpose:update 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(E|Fj)  P(A=1|X1,X2,T=Ti) • 1 if d(X1,Ti) < d(X2,Ti) • 0.5 if d(X1,Ti) = d(X2,Ti) • 0 d(X1,Ti) > d(X2,Ti) • Another one is relative distance

  8. Relative distance measure • using the pictorial features distance as the form of the probability When ND=2, At=1 or 2 Psigmoid(A=1|X1,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 C[P(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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