As per my understanding, while learning the features, the Convolution part of the CNN works-on and preserves the 'spatial relationship' between the pixels. But, I want to identify object(s) in my image whose definition might be all scattered throughout the image.By scattered I mean - The identity of object may exist in separate disconnected islands of pixel-sets. Where each of these island in itself is a connected component of pixels.In other words- my 'Cat' exists, not as a whole, but is broken up into pieces.Would traditional CNN still identify my 'Cat'? If yes, upto what level? If my Cat is broken into 4 pieces? 100 pieces?If not, what adjustment(s) do I need to make so that I can solve this problem?Thanks,
CNNs for identifying scattered object?
machine learning;deep learning;convnet;image classification