Question

Given an image, I would like to extract more subimages from it, but the resulting subimages must not be overly similar to each other. If the center of each ROI should be chosen randomly, then we must make sure that each subimage has at most only a small percentage of area in common with other subimages.
Or we could decompose the image into small regions over a regular grid, then I randomly choose a subimage within each region. This option, however, does not ensure that all subimages are sufficiently different from each other. Obviously I have to choose a good way to compare the resulting subimages, but also a similarity threshold.

The above procedure must be performed on many images: all the extracted subimages should not be too similar. Is there a way to identify regions that are not very similar from a set of images (for eg by inspecting all histograms)?

Answer 1

One possible way is to split your image into n x n squares (save edge cases) as you pointed out, reduce each of them to a single value and group them according to k-nearest values (pertaining to the other pieces). After you group them, then you can select, for example, one image from each group. Something that is potentially better is to use a more relevant metric inside each group, see Comparing image in url to image in filesystem in python for two such metrics. By using this metric, you can select more than one piece from each group.

Here is an example using some duck I found around. It considers n = 128. To reduce each piece to a single number, it calculates the euclidean distance to a pure black piece of n x n.

f = Import["http://fohn.net/duck-pictures-facts/mallard-duck.jpg"];
pieces = Flatten[ImagePartition[ColorConvert[f, "Grayscale"], 128]]

black = Image[ConstantArray[0, {128, 128}]];
dist = Map[ImageDistance[#, black, DistanceFunction -> EuclideanDistance] &,
            pieces];
nf = Nearest[dist -> pieces];

Then we can see the grouping by considering k = 2:

GraphPlot[
 Flatten[Table[
   Thread[pieces[[i]] -> nf[dist[[i]], 2]], {i, Length[pieces]}]],
 VertexRenderingFunction -> (Inset[#2, #, Center, .4] &), 
 SelfLoopStyle -> None]

Now you could use a metric (better than the distance to black) inside each of these groups to select the pieces you want from there.