{"push_id":758924581,"size":1,"distinct_size":1,"ref":"refs/heads/master","head":"9b97adba3b849bb6439e5e71aeac551c4bb894de","before":"3bc6f967d93eb1ff5f30745ee07c9caef0b8fb0e","commits":[{"sha":"9b97adba3b849bb6439e5e71aeac551c4bb894de","author":{"email":"e8ccef0d579672eb34486e8afa814902e12059cf@gmail.com","name":"Scott Bronikowski"},"message":"14 Aug 15\n\n--Did good/bad plots of old test_step_4 run on the newly filtered images.  Saved\n  it in good-bad-comparison/20150814-8bin_chist-center_patch-self_filtering.\n  Nothing looks any better than previous stuff.  Need to try something\n  different.\n\n--Talking with Jeff--after testing the color histogram on sets of randomly\n  chosen images from each class, look at how the groups are compared.\n  Currently making matrix of image-to-image comparisons between each class and\n  then doing things with row/column means/maxes/mins/etc.  Instead, forget the\n  matrix and just rank order all the image-to-image distance scores and take\n  the top X percent, and average that as the similarity between the two groups.\n\n--Ran 15 iterations of the test of color histogram distance (measured both with\n  chi squared and earth mover distance, and both using the full image and a 7x7\n  patch in the center) for 3 random images from each of the 8 classes. (file is\n  histogram_testing.m)\n\n--Results are in source/sentence-codetection/color-histogram-test-results.  The\n  leading number of the filename is the iteration number, and the rest of it\n  should be self-explanatory.  I also saved the random images themselves: by\n  row, they are: chair, bag-book, table, cone, stool, box, bag-shop, junk.\n\n--I think what the results show is that the quality of the match within or the\n  difference between classes is highly dependent upon which images are randomly\n  chosen.  What I'm not entirely sure of is how this will affect the new method\n  of finding similarity between groups of images that may or may not be of the\n  same class (mentioned above).\n\n--It also looks like using the small patch in the center leads to higher\n  variance among the scores--which makes sense since a small patch is more\n  likely to contain fewer colors.  But what I see looking at the images is that\n  the primary object isn't always centered in the proposal image, so sometimes\n  we end up getting a background color instead.  I've thought about sampling the\n  image from more than just the center, for example taking a small set of\n  patches (perhaps 5 in a 'x' or '+' shaped pattern), but I'm not sure if that\n  will cause more harm than good.","distinct":true,"url":"https://api.github.com/repos/scottbronikowski/codetection/commits/9b97adba3b849bb6439e5e71aeac551c4bb894de"}]}