1. Manisha Gupta PHD Student: Fawad Casian Mentor: Dr. Ali Borji

2.  A more accurate way would be using the book that has been fixated instead of a 3 x 3 grid. Simply find the book which its center is closest to the fixation location  Have you discarded the last fixation?  We did not. actually it was one of the reviewers point. then we count total number of times we have fixations on the targets and there were, small enough to not improve the results.  Jitter the fixated locations and see if performance is still the same (or use fixations of a different target for another target)  Show the five targets so we can see how different they are  Manisha: I am writing a script that will open all five targets right now  so, if the target was bird then did the subject looked more at birds? can you predict the category of the animal from fixated locations?  Hosna: yes, this happened. even at the time I wrote a code for image retrieval and it works very nice.  is there a systematic search difference over three collage types in terms of eye movement statistics?  There is chance that subjects might have been using text instead of animal shapes? how can you check this?  Hosna: This I am not sure. just incase they would like to make their job more difficult. but its good to use text clues as well for prediction.  which features subjects relied more on? edges, color, etc?  Hosna: amazon, context, oreily color, mugshots almost nothing  Distribution of saccade length. Does it follow the Ziff/Power law?  Can we predict the collage type from fixation durations?  According to the paper -. To minimise lingering on search taget, participants were put under time pressure and had to find the target and press a confirmation button as quickly as possible. This resulted in lingering of 2.45% for Amazon (O’Reilly: 1.2%, mugshots: 0.35%).  trying different types of features? gist, HOG, VGG, ALEXNET, etc  gist and hog didn't give me better results than color. but I used image patch maybe if we use whole book covers then they can improve the results. or maybe mixture of those  histogram of fixation durat  optimizing K for each subject is dangerous and could lead to overfitting! I think a non parametric way would have been much more appropriate here  Hosna: we had per subject and across subject prediction. thats why we optimize K for each subject in the first task.

4.  Debug code and save book cover extractions  Sort training images from testing images  Meet Dr. Borji about devising a deep learning method