2016-11-09 15:12:56 -0600 | asked a question | Advice on Haar/LBP approach Hi everybody, I'm new to Haar/LBP classifiers and hoping someone can give me advice for a small project I'm working on. I have been reading a lot on this forum and trained a number of classifiers, but the results are not satisfactory with a lot of false positives. I would greatly appreciate any comments that could give me some guidance in selecting the best approach to reach my goal. Also, I hope others can benefit from my question too! Goal: I would like my HAAR/LBP classifier to detect which age-rating a movie has by looking for the age-rating logo: for example, see the '16' logo on this cover. (if somehow this is not a good idea with HAAR/LBP, please let me know :)). For this question, let's only focus on finding age-rating 16. I tried three alternative approaches until now. First approach Positive image: I found a .PNG file which contains the exact logo that also appears on the cover. Negative images: I downloaded thousands of plant and animal pictures from image-net.org. I cat them into the file negatives.dat Creating positive training samples: I used the following command to superimpose my positive image onto random negatives: Create .vec: Training the classifier: Second approach: Identical to the first, except I cropped about ten age-rating 16 - logo's from actual movie cover pics (as opposed to using that 1 PNG-file as my sole positive image). Then I wrote a script that grabs each of these logo's and with opencv_createsamples superimposes them on 500 random negatives to create 5000 positive training images. Third approach: Positive images: Downloaded about 90 movie covers with age rating 16, then with opencv_annotations I indicated the ROI in each image, resulting in the file annotations.txt Negatives images: I use about 400 plant/animal images I downloaded earlier from image-net. Also, I cropped the 90 movie covers to remove the bottom part that contains the age rating and added those to the negatives. Catted everything to negatives.dat Create .Vec: Train Cascade: The results until now, for all approaches, are not satisfactory with a lot of false positives when I feed unseen 16+ rated movie covers to my classifier. I also trained with different numbers of positives and negatives (though pos 1500 neg 3000 has been my maximum). I would appreciate it if someone could let me ... (more) |