Post is related to accuracy of Weka tool to segment images by using classifier trained (and working fine) on one image and using it on another image (where in this case it doesn’t segment well at all)
I am using Weka segmentation to segment stained cell lines.
I trained classifier with multiple features on signal vs background pixels. It works fine on the training image but once applied to another image > it doesn’t work fine.
see images below… red arrows shows bad/missed segmentation… image with blue arrows is the good one working fine.
When I do opposite, using the 2nd image for training > its classifier works fine on it but segment very bad on 1st image.
Q1- how can I solve/enhance this issue ?
Q2- Another question, Segmenting each image with its own trained classifier (will take time) works better but will lead to Non-comparable results…right ???
as I believe they should be segmented with same algorithm/features to avoid variability in segmentation
Q3- Re-training first the classifier with its data on different images and updating it to have better segmentation then using it again to classify all the training-used images, would it work better ? would it do over segmentation ?
Sorry for long topic and many questions.
Thanks in advance for help