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Hi all, I'm familiar with some of the state of the art methods for detecting classes of objects in an image (ie, Cars, humans, CIFAR-classes, etc...). However, if I am going to try to recognize a specific object in an image instead of a class of objects, it seems like a much simpler problem. Suppose I have an object in my room and I want my system to recognize this object when it is in an image (preferably with some invariance to rotation/lighting conditions), are there any algorithms that can take relatively few input examples of my object and then learn to recognize it in images? Unless I'm remembering wrong, I thought I had seen algorithms that could do this before... Any help pointing me in the right direction would be appreciated. Thanks! |
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I think this is a standard image processing problem rather than machine learning see the SIFT paper http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf... figures 12 and 13 for example |
I'm not a vision person, but my impression is that any method that works for classes of objects should work for the same object if trained on that kind of data and tuned well.
I don't think the problem pointed out here pertains to simple recognition of an object in an image. It is a lot more complex as it also involves focus of attention to the object of interest.
To my knowledge, the present day deep learning methods (which are the state of the art models) are still focusing on feed-forward models. With in this scene segmentation is the closest that I could think of that is related to the problem here. Check http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf
Another more recent paper related to attention is http://www.eng.uwaterloo.ca/~jbergstr/files/nips_dl_2012/Paper%2038.pdf