0
$\begingroup$

I am working on an image classification tasks in which I have 4 classes (For example A, B, C, D). I used CNN model (transfer learning) to train the model and predict the video frames.

Ideally, there would not be any sudden transition from one class to another. Ideal Transition would be as given below:
A A A A A ..... A A A A B B B B B B ..... B B B B B B B D D D D D ..... D D D C C C C C C C ............. C C C

However when predicting using the trained model, I could see some frames being mis-classified. For example I given an video input of Class A which has around 30 frames. In that 30 frames 5 would be predicted as Class C or D.

How would I make the smooth transition from one class to another. There should be sufficient number of evidences so that I can make transition from one class to another.

As of now I found moving average technique which does similar kind of smoothening. I would like to know is there any other method which is more related to probability.

Kindly let me know if you need more details.

Thank you, KK

$\endgroup$
2
  • 1
    $\begingroup$ The moving average is probability-related enough $\endgroup$ Commented Apr 17, 2019 at 5:00
  • $\begingroup$ Thanks for the input.. Is there any other way.?? $\endgroup$
    – deepguy
    Commented Apr 19, 2019 at 8:13

1 Answer 1

1
$\begingroup$

An option could be using the output of the i-th image as an input for classifying the (i+1)-th image.

$\endgroup$
1
  • $\begingroup$ Thanks for the input. Doesn't that become an RNN? $\endgroup$
    – deepguy
    Commented Apr 22, 2019 at 14:32

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.