Hello there,
I am attempting to train and test a new image classification algorithm using the R
package MLWIC
provided in your recent paper, Tabak et al. 2018. I am an experienced R
user but do not have experience in python
.
I'm trying to process images to ready them for training and testing. In the Tabak et al 2018 paper, it mentions that authors followed methods in your recent publication (Norouzzadeh et al 2018 - appendix)
I have a few questions.
1. Am I correct in assuming that the train
command in MLWIC
performs "random cropping, horizontal flipping, brightness modification, and contrast modification" to each training image, as is recommended in both papers?
There is code provided within an "L1" folder that does seem to do this, but it's not clear (to me) if this code is leveraged during MLWIC::train
. I did ask Mikey Tabak about this (here) and I wondered if you could provide any further clarification.
2. How is image normalization carried out for the test dataset?
When I read through the Norouzzadeh et al 2018 appendix (linked above) there is a section on the second page, second paragraph, that states:
After scaling down the images, we computed the mean and standard deviation of pixel intensities for each color channel separately and then we normalized the images by subtracting the average and dividing by the standard deviation.
I did some reading and found that some authors use the mean and standard deviation of the entire dataset, while others use the mean and standard deviation of each image. Forgive me this question is naive... (I am, after all, the intended "ecologist-not-data-scientist" audience for the program ๐) - did you use the mean and sd of each image, or of the whole dataset?
I have one final question but I feel that it is more general, so I've posted it on stackoverflow. If you do have time to check it out there, I would be very grateful for your two cents.
https://stackoverflow.com/questions/55306443/normalizing-an-image-in-r-using-mean-and-standard-deviation