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I am trying to calculate EMD (a.k.a. Wasserstein Distance) for these two grayscale (299x299) images/heatmaps: Right now, I am calculating the histogram/distribution of both images. The histograms will be a vector of size 256 in which the nth value indicates the percent of the pixels in the image with the given darkness level. Then, using these to histograms, I am calculating the EMD using the function wasserstein_distance from scipy.stats.

However, I am now comparing only the intensity of the images, but I also need to compare the location of the intensity of the images.

How can I do this?

I am thinking about obtaining a histogram for every row of the images (which results in 299 histograms per image) and then calculating the EMD 299 times and take the average of these EMD's to get a final score. Is this the right way to go?

Other methods to calculate the similarity bewteen two grayscale are also appreciated.

I am trying to calculate EMD (a.k.a. Wasserstein Distance) for these two grayscale (299x299) images/heatmaps: Right now, I am calculating the histogram/distribution of both images. The histograms will be a vector of size 256 in which the nth value indicates the percent of the pixels in the image with the given darkness level. Then, using these to histograms, I am calculating the EMD using the function wasserstein_distance from scipy.stats.

However, I am now comparing only the intensity of the images, but I also need to compare the location of the intensity of the images.

How can I do this?

I am thinking about obtaining a histogram for every row of the images (which results in 299 histograms per image) and then calculating the EMD 299 times and take the average of these EMD's to get a final score. Is this the right way to go?

I am trying to calculate EMD (a.k.a. Wasserstein Distance) for these two grayscale (299x299) images/heatmaps: Right now, I am calculating the histogram/distribution of both images. The histograms will be a vector of size 256 in which the nth value indicates the percent of the pixels in the image with the given darkness level. Then, using these to histograms, I am calculating the EMD using the function wasserstein_distance from scipy.stats.

However, I am now comparing only the intensity of the images, but I also need to compare the location of the intensity of the images.

How can I do this?

I am thinking about obtaining a histogram for every row of the images (which results in 299 histograms per image) and then calculating the EMD 299 times and take the average of these EMD's to get a final score. Is this the right way to go?

Other methods to calculate the similarity bewteen two grayscale are also appreciated.

1

Calculate Earth Mover's Distance for two grayscale images

I am trying to calculate EMD (a.k.a. Wasserstein Distance) for these two grayscale (299x299) images/heatmaps: Right now, I am calculating the histogram/distribution of both images. The histograms will be a vector of size 256 in which the nth value indicates the percent of the pixels in the image with the given darkness level. Then, using these to histograms, I am calculating the EMD using the function wasserstein_distance from scipy.stats.

However, I am now comparing only the intensity of the images, but I also need to compare the location of the intensity of the images.

How can I do this?

I am thinking about obtaining a histogram for every row of the images (which results in 299 histograms per image) and then calculating the EMD 299 times and take the average of these EMD's to get a final score. Is this the right way to go?