Simple and fast method to compare images for similarity

Question

I need a simple and fast way to compare two images for similarity  I e  I want to get a high value if they contain exactly the same thing but may have some slightly different background and may be moved   resized by a few pixel    More concrete  if that matters  The one picture is an icon and the other picture is a subarea of a screenshot and I want to know if that subarea is exactly the icon or not    I have OpenCV at hand but I am still not that used to it   One possibility I thought about so far  Divide both pictures into 10x10 cells and for each of those 100 cells  compare the color histogram  Then I can set some made up threshold value and if the value I get is above that threshold  I assume that they are similar   I haven t tried it yet how well that works but I guess it would be good enough  The images are already pretty much similar  in my use case   so I can use a pretty high threshold value   I guess there are dozens of other possible solutions for this which would work more or less  as the task itself is quite simple as I only want to detect similarity if they are really very similar   What would you suggest     There are a few very related   similar questions about obtaining a signature fingerprint hash from an image    OpenCV   SURF How to generate a image hash   fingerprint   signature out of the descriptors  Image fingerprint to compare similarity of many images Near-Duplicate Image Detection OpenCV  Fingerprint Image and Compare Against Database  more  more  more  more  more  more  more   Also  I stumbled upon these implementations which have such functions to obtain a fingerprint    pHash imgSeek  GitHub repo   GPL  based on the paper Fast Multiresolution Image Querying image-match  Very similar to what I was searching for  Similar to pHash  based on An image signature for any kind of image  Goldberg et al  Uses Python and Elasticsearch  iqdb ImageHash  supports pHash  Image Deduplicator  imagededup   Supports CNN  PHash  DHash  WHash  AHash    Some discussions about perceptual image hashes  here    A bit offtopic  There exists many methods to create audio fingerprints  MusicBrainz  a web-service which provides fingerprint-based lookup for songs  has a good overview in their wiki  They are using AcoustID now  This is for finding exact  or mostly exact  matches  For finding similar matches  or if you only have some snippets or high noise   take a look at Echoprint  A related SO question is here  So it seems like this is solved for audio  All these solutions work quite good   A somewhat more generic question about fuzzy search in general is here  E g  there is locality-sensitive hashing and nearest neighbor search

User · Answer

If for matching identical images - code for L2 distance

// Compare two images by getting the L2 error (square-root of sum of squared error).
double getSimilarity( const Mat A, const Mat B ) {
if ( A.rows > 0 && A.rows == B.rows && A.cols > 0 && A.cols == B.cols ) {
    // Calculate the L2 relative error between images.
    double errorL2 = norm( A, B, CV_L2 );
    // Convert to a reasonable scale, since L2 error is summed across all pixels of the image.
    double similarity = errorL2 / (double)( A.rows * A.cols );
    return similarity;
}
else {
    //Images have a different size
    return 100000000.0;  // Return a bad value
}

Fast. But not robust to changes in lighting/viewpoint etc. Source

User · Answer

If you can be sure to have precise alignment of your template  the icon  to the testing region  then any old sum of pixel differences will work    If the alignment is only going to be a tiny bit off  then you can low-pass both images with cv  GaussianBlur before finding the sum of pixel differences   If the quality of the alignment is potentially poor then I would recommend either a Histogram of Oriented Gradients or one of OpenCV s convenient keypoint detection descriptor algorithms  such as SIFT or SURF

User · Answer

If you want to get an index about the similarity of the two pictures  I suggest you from the metrics the SSIM index  It is more consistent with the human eye  Here is an article about it  Structural Similarity Index  It is implemented in OpenCV too  and it can be accelerated with GPU  OpenCV SSIM with GPU

User · Answer

If you want to compare image for similarity I suggest you to used OpenCV  In OpenCV  there are few feature matching and template matching  For feature matching  there are SURF  SIFT  FAST and so on detector  You can use this to detect  describe and then match the image  After that  you can use the specific index to find number of match between the two images

User · Answer

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

User · Answer

I face the same issues recently  to solve this problem simple and fast algorithm to compare two images  once and for all  I contribute an img hash module to opencv contrib  you can find the details from this link    img hash module provide six image hash algorithms  quite easy to use   Codes example  origin lena  blur lena  resize lena  shift lena   include  lt opencv2 core hpp gt   include  lt opencv2 core ocl hpp gt   include  lt opencv2 highgui hpp gt   include  lt opencv2 img hash hpp gt   include  lt opencv2 imgproc hpp gt    include  lt iostream gt   void compute cv  Ptr lt cv  img hash  ImgHashBase gt  algo        auto input   cv  imread  lena png        cv  Mat similar img         detect similiar image after blur attack     cv  GaussianBlur input  similar img   7 7   2  2       cv  imwrite  lena blur png   similar img       cv  Mat hash input  hash similar      algo- gt compute input  hash input       algo- gt compute similar img  hash similar       std  cout lt  lt  gaussian blur attack     lt  lt                 algo- gt compare hash input  hash similar  lt  lt std  endl         detect similar image after shift attack     similar img setTo 0       input cv  Rect 0 10  input cols input rows-10                copyTo similar img cv  Rect 0 0 input cols input rows-10         cv  imwrite  lena shift png   similar img       algo- gt compute similar img  hash similar       std  cout lt  lt  shift attack     lt  lt                 algo- gt compare hash input  hash similar  lt  lt std  endl         detect similar image after resize     cv  resize input  similar img   120  40        cv  imwrite  lena resize png   similar img       algo- gt compute similar img  hash similar       std  cout lt  lt  resize attack     lt  lt                 algo- gt compare hash input  hash similar  lt  lt std  endl     int main         using namespace cv  img hash         disable opencl acceleration may or may not  boost up speed of img hash     cv  ocl  setUseOpenCL false          if the value after compare  lt   8  that means the images       very similar to each other     compute ColorMomentHash  create            there are other algorithms you can try out       every algorithms have their pros and cons     compute AverageHash  create         compute PHash  create         compute MarrHildrethHash  create         compute RadialVarianceHash  create           BlockMeanHash support mode 0 and mode 1  they associate to       mode 1 and mode 2 of PHash library     compute BlockMeanHash  create 0        compute BlockMeanHash  create 1        In this case  ColorMomentHash give us best result   gaussian blur attack   0 567521  shift attack   0 229728  resize attack   0 229358   Pros and cons of each algorithm    The performance of img hash is good too  Speed comparison with PHash library 100 images from ukbench     If you want to know the recommend thresholds for these algorithms  please check this post http   qtandopencv blogspot my 2016 06 introduction-to-image-hash-module-of html   If you are interesting about how do I measure the performance of img hash modules include speed and different attacks   please check this link http   qtandopencv blogspot my 2016 06 speed-up-image-hashing-of-opencvimghash html

User · Answer

Does the screenshot contain only the icon  If so  the L2 distance of the two images might suffice  If the L2 distance doesn t work  the next step is to try something simple and well established  like  Lucas-Kanade  Which I m sure is available in OpenCV

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