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The Bayesian Image Retrieval System,PicHunter

PicHunter utilizes Bayes' rule to predict goal images based on user actions, facilitating exact or category searches and browsing. It assesses image similarity through relevance feedback and display updating models for efficient retrieval. Theoretical basis for PicHunter is explained with experimental setups and results analysis.

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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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