.net – Image Comparison for find percentage of similarity between images

InvariantCulture

Uses a "standard" set of character orderings (a,b,c, ... etc.). This is in contrast to some specific locales, which may sort characters in different orders ('a-with-acute' may be before or after 'a', depending on the locale, and so on).

Ordinal

On the other hand, looks purely at the values of the raw byte(s) that represent the character.

There's a great sample at http://msdn.microsoft.com/en-us/library/e6883c06.aspx that shows the results of the various StringComparison values. All the way at the end, it shows (excerpted):

StringComparison.InvariantCulture:
LATIN SMALL LETTER I (U+0069) is less than LATIN SMALL LETTER DOTLESS I (U+0131)
LATIN SMALL LETTER I (U+0069) is less than LATIN CAPITAL LETTER I (U+0049)
LATIN SMALL LETTER DOTLESS I (U+0131) is greater than LATIN CAPITAL LETTER I (U+0049)

StringComparison.Ordinal:
LATIN SMALL LETTER I (U+0069) is less than LATIN SMALL LETTER DOTLESS I (U+0131)
LATIN SMALL LETTER I (U+0069) is greater than LATIN CAPITAL LETTER I (U+0049)
LATIN SMALL LETTER DOTLESS I (U+0131) is greater than LATIN CAPITAL LETTER I (U+0049)

You can see that where InvariantCulture yields (U+0069, U+0049, U+00131), Ordinal yields (U+0049, U+0069, U+00131).

Can the screenshot or icon be transformed (scaled, rotated, skewed ...)? There are quite a few methods on top of my head that could possibly help you:

• Simple euclidean distance as mentioned by @carlosdc (doesn't work with transformed images and you need a threshold).
• (Normalized) Cross Correlation - a simple metrics which you can use for comparison of image areas. It's more robust than the simple euclidean distance but doesn't work on transformed images and you will again need a threshold.
• Histogram comparison - if you use normalized histograms, this method works well and is not affected by affine transforms. The problem is determining the correct threshold. It is also very sensitive to color changes (brightness, contrast etc.). You can combine it with the previous two.
• Detectors of salient points/areas - such as MSER (Maximally Stable Extremal Regions), SURF or SIFT. These are very robust algorithms and they might be too complicated for your simple task. Good thing is that you do not have to have an exact area with only one icon, these detectors are powerful enough to find the right match. A nice evaluation of these methods is in this paper: Local invariant feature detectors: a survey.

Most of these are already implemented in OpenCV - see for example the cvMatchTemplate method (uses histogram matching): http://dasl.mem.drexel.edu/~noahKuntz/openCVTut6.html. The salient point/area detectors are also available - see OpenCV Feature Detection.