1. Silvino Barreiros

2. Cross Validation Bootstrapping Testing Prepared for final submission REU Poster

3. Train on the hardest images Test images with high PFA moved to train set Images in training replaced Keeps the training set from growing to large

4. Steps Extract all positives and and equal number of negatives for training sets Take 100 negatives that we didn't train on as the testing set Compute the codebook once using the training images Use codebook to generate word counts and the classifier

5. Steps continued Test the 100 images in the negative testing set Move the worst false alarms from the negative testing set into the negative training set Add a corresponding number of duplicate positives into the positive training set Replenish the negative testing set with more negatives in order to bring it back to 100 images Iterate at step 4, but don't recompute the codebook! Use the original codebook, but generate new wordcounts using the additional training images.

6. Features Tested Airplane_flying, Bus, Dog, Doorway and Person-eating Results Each iteration produces varying results Reasons Not enough positive images used Images moved from the training set are better then ones moved in

7. IterationPDPFATrain SetTest Set 157.14%28%58100 285.71%22%86100 371.43%12%108100 471.43%10%120100 585.71%6%130100 671.43%6%136100 757.14%3%142100 857.14%8%145100 AVG69.64%11.875% Airplane_flying

8. IterationPDPFATrain SetTest Set 160%64%90100 250%19%134100 370%20%153100 440%14%173100 550%5%187100 660%9%192100 760%11%201100 870%8%212100 AVG57.5%18.75% Chair

9. IterationPDPFATrain SetTest Set 170%34%90100 250%20%123100 350%19%143100 470%12%162100 580%9%174100 650%7%183100 770%14%190100 850%8%204100 AVG61.25%15.375% Infant

10. Started testing on harder features Airplane Flying, Doorway… etc After testing complete change parameters

11. Method to test positive images Group positive images Cycle one group to test and train on others

12. Crunch time!! Final Run Complete submission code