1. 1 Adaptive relevance feedback based on Bayesian inference for image retrieval Reporter : Erica Li Date : 2005.3.17

2. 2 1. Introduction Rich get richer (RGR): If the feedback images of current iteration are consistent with the previous ones, the images that are similar to the query target are guaranteed to have higher updated relevance probabilities. RGR consistent the image retrieval process as a problem of determining image’s probabilities being relevant to the user’s query intention.

3. 3 2. Bayesian relevance feedback(1/2) : positive examples : negative examples Under the Bayesian rule, the posterior probabilities of an image x in the database can be calculated as

4. 4 2. Bayesian relevance feedback(2/2 ) t : feedback time : prior probabilities : class-conditional PDFs : posterior probabilities : the cumulated image set from the interaction 1 to t.

5. 5 3.RGR 3.1. RGR strategy In this way, an image that is similar to the target according to the query at time t-1 and similar to the positive examples at time is emphasized.

6. 6 3.2. Algorithm description of RGR(1/2) The Query images set : Initial : positive example set includes all images of Q,and the negative example set is. are set to 0.5 Approximate the PDF for : There are no sufficient examples The PDF value for the image x is set as the normalized distance between the image x and the centroid of.

7. 7 3.2. Algorithm description of RGR(2/2)

8. 8 4. Experiments Three test sets form Corel: 800 images, 32 categories 10000 images, 100 categories 1500 images, 15 categories

9. 9 Fig 1.

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