I am working on image binarization for my undergraduate project. The idea is to convert an RGB image to a binary image while retaining maximum features. I presented a comparison of the results of my algorithm and Otsu's algorithm for the project presentation (i.e. the output images of both algorithms, so visual inspection could be done). On visual inspection, Otsu's algorithm results have lesser features when compared to the algorithm I developed.
The professors who reviewed our work said visual inspection is not allowed, I need quantifiable metrics to prove the algorithm I worked on is better. Image binarization is a kind of segmentation, based on the literature review I have done it is different from binary image segmentation, which requires separating object from the background.
I found this: https://github.com/xuebinqin/Binary-Segmentation-Evaluation-Tool which is used to evaluate binary image segmentation accuracy. This technique uses a ground truth which has the object colored white and the background colored black. The results are compared with the ground truths using various techniques. I also found the Berkeley Segmentation Dataset which has ground truths and scripts to evaluate segmentation accuracy. However, these do not seem to be useful for my purpose because the goal of my project is different. I don't know what metrics I should use for my project.
Are there any such metrics?
Any ideas on this are welcome.