I have a scanned image, and they need to be classified in one of the pre-defined image classes, so that it can be sorted. However, the problem is the open nature of the classes. At testing time, new classes of scanned images can be added and the model should not only classify them as unseen (open set image recognition), but it should be able to tell in which new class it should belong (not able to figure out the implementation for this.)
So, I am thinking that the below option can work for the classification of unseen classes
Zero-shot learning: Once the image is classified as unseen, we can then apply zero-shot learning to find its respective class for sorting.
Template matching: Match the test image of unseen classes with all available class images, and, once we have a match, we can do sorting of images.
Meta learning-based approach: I am not sure how to implement this, suggestions are much appreciated.
Note: I already tried the classical computer vision approach, but it's not working out. So, more open for neural net-based approach.
Is my approach to solving the problem correct? If possible, suggest some alternative to find the corresponding match/classification of the unseen class image. As I could think of these 2 alternative solutions only.