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 F j – 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|F j )?

5. Theoretical basis for PicHunter During each session a set D t of N D images, Action A t H t ----History of the session

6. User Model:Assessing Image similarity Key term: P(A t |T=T i,D t,U) U:specific user Purpose:update the probability of each T i 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|F j )  P(A=1|X 1,X 2,T=T i ) 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 N D =2, A t =1 or 2 P sigmoid (A=1|X 1,X 2,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