I'm trying to build an engine for the following task:

I have n videos, from which I've taken 1 snapshot each. I am trying to train a classification algorithm on these n snapshots. Till now I have only tried to train a basic convolutional net but I think maybe their might be a problem with this approach since all snapshots are closely related, ie. there isn't a clear distinction between all categories.

Is there any other methodology available out there that could make this task simpler?

EDIT: My basic task is to build an app that would play the video associated to the image that is clicked by the user

EDIT: Okay so the problem is that I have a certain number of videos. I am capturing 1 frame per video. Say we give a certain user these, say n frames, 1 for each video. Now when the user captures a certain image, I want to play the video the image is a snapshot of.

My question is: Can this be considered as a classic classification problem? Or is it more of an image matching problem, like what we do in a Siamese network?

  • $\begingroup$ Unfortunately, I can't understand what problem you are trying to solve or what classification task you are trying to use the neural network for. Perhaps you can edit your question to make it clearer and give some examples. Also, a basic CNN seems pretty simple to me already. $\endgroup$ – D.W. Oct 5 '19 at 6:18

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