CODEWITHSUNDEEP
machine-learningcomputer-vision
jmac
jmac

Reputation: 123

Using flipped images for machine learning dataset

I'v got a binary classification problem. I'm trying to train a neural network to recognize objects from images. Currently I've about 1500 50x50 images. The question is whether extending my current training set by the same images flipped horizontally is a good idea or not? (images are not symetric)

Thanks

Upvotes: 2

Views: 2236

Answers (4)

Locked
Locked

Reputation: 186

It depends on what your NN is based on. If you are extracting rotation invariant features or features that do not depend on the spatial position within the the image (like histograms or whatever) and train your NN with these features, then rotating will not be a good idea.

If you are training directly on pixel values, then it might be a good idea. Some more details might be useful.

Upvotes: 0

Krish
Krish

Reputation: 1827

If the images patches are not symmetric I don't think its a good idea to flip. Better idea is to do some similarity transforms to the training set with some limits. Another way to increase the dataset is to add gaussian smoothed templates to it. Make sure that the number of positive and negative samples are proportional. Too many positive and too less negative might skew the classifier and give bad performance on testing set.

Upvotes: 0

SlimJim
SlimJim

Reputation: 2272

I have used flipped images in AdaBoost with great success in the course: http://www.csc.kth.se/utbildning/kth/kurser/DD2427/bik12/Schedule.php from the zip "TrainingImages.tar.gz".

I know there are some information on pros/cons with using flipped images somewhere in the slides (at the homepage) but I can't find it. Also a great resource is http://www.csc.kth.se/utbildning/kth/kurser/DD2427/bik12/DownloadMaterial/FaceLab/Manual.pdf (together with the slides) going thru things like finding things in different scales and orientation.

Upvotes: 1

Kiril
Kiril

Reputation: 40345

I think you can do this to a much larger extent, not just flipping the images horizontally, but changing the angle of the image by 1 degree. This will result in 360 samples for every instance that you have in your training set. Depending on how fast your algorithm is, this may be a pretty good way to ensure that the algorithm isn't only trained to recognize images and their mirrors.

It's possible that it's a good idea, but then again, I don't know what's the goal or the domain of the image recognition. Let's say the images contain characters and you're asking the image recognition software to determine if an image contains a forward slash / or a back slash \ then flipping the image will make your training data useless. If your domain doesn't suffer from such issues, then I'd think it's a good idea to flip them and even rotate with varying degrees.

Upvotes: 1

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