How can I measure the similarity between two images

Question

I would like to compare a screenshot of one application  could be a Web page  with a previously taken screenshot to determine whether the application is displaying itself correctly  I don t want an exact match comparison  because the aspect could be slightly different  in the case of a Web app  depending on the browser  some element could be at a slightly different location   It should give a measure of how similar are the screenshots   Is there a library   tool that already does that  How would you implement it

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Well  not to answer your question directly  but I have seen this happen  Microsoft recently launched a tool called PhotoSynth which does something very similar to determine overlapping areas in a large number of pictures  which could be of different aspect ratios    I wonder if they have any available libraries or code snippets on their blog

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You could use a pure mathematical approach of O n 2   but it will be useful only if you are certain that there s no offset or something like that   Although that if you have a few objects with homogeneous coloring it will still work pretty well    Anyway  the idea is the compute the normalized dot-product of the two matrices  C   sum Pij Qij  2  sum Pij 2  sum Qij 2     This formula is actually the  cosine  of the angle between the matrices  wierd    The bigger the similarity  lets say Pij Qij   C will be 1  and if they re completely different  lets say for every i j Qij   1  avoiding zero-division   Pij   255  then for size nxn  the bigger n will be  the closer to zero we ll get   By rough calculation  C 1 n 2

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If this is something you will be doing on an occasional basis and doesn t need automating  you can do it in an image editor that supports layers  such as Photoshop or Paint Shop Pro  probably GIMP or Paint Net too  but I m not sure about those    Open both screen shots  and put one as a layer on top of the other   Change the layer blending mode to Difference  and everything that s the same between the two will become black   You can move the top layer around to minimize any alignment differences

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Don t video encoding algorithms like MPEG compute the difference between each frame of a video so they can just encode the delta   You might look into how video encoding algorithms compute those frame differences   Look at this open source image search application http   www semanticmetadata net lire    It describes several image similarity algorighms  three of which are from the MPEG-7 standard  ScalableColor  ColorLayout  EdgeHistogram and Auto Color Correlogram

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Well a really base-level method to use could go through every pixel colour and compare it with the corresponding pixel colour on the second image - but that s a probably a very very slow solution

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You can use Siamese Network to see if the two images are similar or dissimilar following this tutorial  This tutorial cluster the similar images whereas you can use L2 distance to measure the similarity of two images

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This depends entirely on how smart you want the algorithm to be   For instance  here are some issues    cropped images vs  an uncropped image images with a text added vs  another without mirrored images   The easiest and simplest algorithm I ve seen for this is just to do the following steps to each image    scale to something small  like 64x64 or 32x32  disregard aspect ratio  use a combining scaling algorithm instead of nearest pixel scale the color ranges so that the darkest is black and lightest is white rotate and flip the image so that the lighest color is top left  and then top-right is next darker  bottom-left is next darker  as far as possible of course    Edit A combining scaling algorithm is one that when scaling 10 pixels down to one will do it using a function that takes the color of all those 10 pixels and combines them into one  Can be done with algorithms like averaging  mean-value  or more complex ones like bicubic splines   Then calculate the mean distance pixel-by-pixel between the two images   To look up a possible match in a database  store the pixel colors as individual columns in the database  index a bunch of them  but not all  unless you use a very small image   and do a query that uses a range for each pixel value  ie  every image where the pixel in the small image is between -5 and  5 of the image you want to look up   This is easy to implement  and fairly fast to run  but of course won t handle most advanced differences  For that you need much more advanced algorithms

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How to measure similarity between two images entirely depends on what you would like to measure  for example  contrast  brightness  modality  noise    and then choose the best suitable similarity measure there is for you  You can choose from MAD  mean absolute difference   MSD  mean squared difference  which are good for measuring brightness   there is also available CR  correlation coefficient  which is good in representing correlation between two images  You could also choose from histogram based similarity measures like SDH  standard deviation of difference image histogram  or multimodality similarity measures like MI  mutual information  or NMI  normalized mutual information     Because this similarity measures cost much in time  it is advised to scale images down before applying these measures on them

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Beyond Compare has pixel-by-pixel comparison for images  e g

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There s software for content-based image retrieval  which does  partially  what you need  All references and explanations are linked from the project site and there s also a short text book  Kindle   LIRE

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to expand on Vaibhav s note  hugin is an open-source  autostitcher  which should have some insight on the problem

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I wonder  and I m really just throwing the idea out there to be shot down  if something could be derived by subtracting one image from the other  and then compressing the resulting image as a jpeg of gif  and taking the file size as a measure of similarity   If you had two identical images  you d get a white box  which would compress really well  The more the images differed  the more complex it would be to represent  and hence the less compressible   Probably not an ideal test  and probably much slower than necessary  but it might work as a quick and dirty implementation

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Use a normalised colour histogram   Read the section on applications here   they are commonly used in image retrieval matching systems and are a standard way of matching images that is very reliable  relatively fast and very easy to implement   Essentially a colour histogram will capture the colour distribution of the image  This can then be compared with another image to see if the colour distributions match   This type of matching is pretty resiliant to scaling  once the histogram is normalised   and rotation shifting movement etc   Avoid pixel-by-pixel comparisons as if the image is rotated shifted slightly it may lead to a large difference being reported   Histograms would be straightforward to generate yourself  assuming you can get access to pixel values   but if you don t feel like it  the OpenCV library is a great resource for doing this kind of stuff  Here is a powerpoint presentation that shows you how to create a histogram using OpenCV

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You ll need pattern recognition for that  To determine small differences between two images  Hopfield nets work fairly well and are quite easy to implement  I don t know any available implementations  though

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The  classic  way of measuring this is to break the image up into some canonical number of sections  say a 10x10 grid  and then computing a histogram of RGB values inside of each cell and compare corresponding histograms  This type of algorithm is preferred because of both its simplicity and it s invariance to scaling and  small   translation

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You might look at the code for the open source tool findimagedupes  though it appears to have been written in perl  so I can t say how easy it will be to parse     Reading the findimagedupes page that I liked  I see that there is a C   implementation of the same algorithm  Presumably this will be easier to understand   And it appears you can also use gqview

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A ruby solution can be found here  From the readme   Phashion is a Ruby wrapper around the pHash library   perceptual hash   which detects duplicate and near duplicate multimedia files

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