How to compare image similarity using php regardless of scale, rotation?

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I want to compare similarity between below images. Acording to my requirements I want to identify all of these images as similar, since it has use the same color, same clip art. The only difference in these images are rotation ,scale and the placement of the clip art. Since all 3 t-shirts has used the same color and clip art I want to identify all 3 images as similar. I tried out the method described in hackerfactor.com. But it doesn't give me correct result acording to my requirements. How to identify all these images as similar?DO you have any suggestions? Please help me.

The below images should be recognized as different from above images.(Even though the tshirts has the same color, clip arts are different. Last tshirt is different from above, because it has use the same clip art, but twice. )

Moved to GitHub

Because this question is quite interesting, I moved the whole thing to GitHub where you can find the current implementation: ImageCompare

Original answer

I made a very simple approach, using img-resize and comparing the average color of the resized images.

$binEqual = [
    file_get_contents('http://i.stack.imgur.com/D8ct1.png'),
    file_get_contents('http://i.stack.imgur.com/xNZt1.png'),
    file_get_contents('http://i.stack.imgur.com/kjGjm.png')
];

$binDiff = [
    file_get_contents('http://i.stack.imgur.com/WIOHs.png'),
    file_get_contents('http://i.stack.imgur.com/ljoBT.png'),
    file_get_contents('http://i.stack.imgur.com/qEKSK.png')
];


function getAvgColor($bin, $size = 10) {

    $target = imagecreatetruecolor($size, $size);
    $source = imagecreatefromstring($bin);

    imagecopyresized($target, $source, 0, 0, 0, 0, $size, $size, imagesx($source), imagesy($source));

    $r = $g = $b = 0;

    foreach(range(0, $size - 1) as $x) {
        foreach(range(0, $size - 1) as $y) {
            $rgb = imagecolorat($target, $x, $y);
            $r += $rgb >> 16;
            $g += $rgb >> 8 & 255;
            $b += $rgb & 255;
        }
    }   

    unset($source, $target);

    return (floor($r / $size ** 2) << 16) +  (floor($g / $size ** 2) << 8)  + floor($b / $size ** 2);
}

function compAvgColor($c1, $c2, $tolerance = 4) {

    return abs(($c1 >> 16) - ($c2 >> 16)) <= $tolerance && 
           abs(($c1 >> 8 & 255) - ($c2 >> 8 & 255)) <= $tolerance &&
           abs(($c1 & 255) - ($c2 & 255)) <= $tolerance;
}

$perms = [[0,1],[0,2],[1,2]];

foreach($perms as $perm) {
    var_dump(compAvgColor(getAvgColor($binEqual[$perm[0]]), getAvgColor($binEqual[$perm[1]])));
}

foreach($perms as $perm) {
    var_dump(compAvgColor(getAvgColor($binDiff[$perm[0]]), getAvgColor($binDiff[$perm[1]])));
}

For the used size and color-tolerance I get the expected result:

bool(true)
bool(true)
bool(true)
bool(false)
bool(false)
bool(false)
More advanced implementation

Empty T-Shirt to compare:

$binEqual = [
    file_get_contents('http://i.stack.imgur.com/D8ct1.png'),
    file_get_contents('http://i.stack.imgur.com/xNZt1.png'),
    file_get_contents('http://i.stack.imgur.com/kjGjm.png')
];

$binDiff = [
    file_get_contents('http://i.stack.imgur.com/WIOHs.png'),
    file_get_contents('http://i.stack.imgur.com/ljoBT.png'),
    file_get_contents('http://i.stack.imgur.com/qEKSK.png')
];

class Color {
    private $r = 0;
    private $g = 0;
    private $b = 0;

    public function __construct($r = 0, $g = 0, $b = 0)
    {
        $this->r = $r;
        $this->g = $g;
        $this->b = $b;
    }

    public function r()
    {
        return $this->r;
    }

    public function g()
    {
        return $this->g;
    }

    public function b()
    {
        return $this->b;
    }

    public function toInt()
    {
        return $this->r << 16 + $this->g << 8 + $this->b;
    }

    public function toRgb()
    {
        return [$this->r, $this->g, $this->b];  
    }

    public function mix(Color $color)
    {
        $this->r = round($this->r + $color->r() / 2);
        $this->g = round($this->g + $color->g() / 2);
        $this->b = round($this->b + $color->b() / 2);
    }

    public function compare(Color $color, $tolerance = 500)
    {
        list($r1, $g1, $b1) = $this->toRgb();
        list($r2, $g2, $b2) = $color->toRgb();

        $diff = round(sqrt(pow($r1 - $r2, 2) + pow($g1 - $g2, 2) + pow($b1 - $b2, 2)));

        printf("Comp r(%s : %s), g(%s : %s), b(%s : %s) Diff %s \n", $r1, $r2, $g1, $g2, $b1, $b2, $diff);

        return  $diff <= $tolerance;
    }

    public static function fromInt($int) {
        return new self($int >> 16, $int >> 8 & 255, $int & 255);
    }
}


function getAvgColor($bin, $size = 5) {

    $target    = imagecreatetruecolor($size, $size);
    $targetTmp = imagecreatetruecolor($size, $size);

    $sourceTmp = imagecreatefrompng('http://i.stack.imgur.com/gfn5A.png');
    $source    = imagecreatefromstring($bin);

    imagecopyresized($target, $source, 0, 0, 0, 0, $size, $size, imagesx($source), imagesy($source));
    imagecopyresized($targetTmp, $sourceTmp, 0, 0, 0, 0, $size, $size, imagesx($source), imagesy($source));

    $r = $g = $b = $relPx = 0;

    $baseColor = new Color();

    foreach(range(0, $size - 1) as $x) {
        foreach(range(0, $size - 1) as $y) {
            if (imagecolorat($target, $x, $y) != imagecolorat($targetTmp, $x, $y))
                $baseColor->mix(Color::fromInt(imagecolorat($target, $x, $y)));
        }
    }

    unset($source, $target, $sourceTmp, $targetTmp);

    return $baseColor;

}

$perms = [[0,0], [1,0], [2,0], [1,0], [1,1], [1,2], [2,0], [2,1], [2,2]];

echo "Equal\n";
foreach($perms as $perm) {
    var_dump(getAvgColor($binEqual[$perm[0]])->compare(getAvgColor($binEqual[$perm[1]])));
}

echo "Different\n";
foreach($perms as $perm) {
    var_dump(getAvgColor($binEqual[$perm[0]])->compare(getAvgColor($binDiff[$perm[1]])));
}

Result:

Equal
Comp r(101 : 101), g(46 : 46), b(106 : 106) Diff 0 
bool(true)
Comp r(121 : 101), g(173 : 46), b(249 : 106) Diff 192 
bool(true)
Comp r(219 : 101), g(179 : 46), b(268 : 106) Diff 241 
bool(true)
Comp r(121 : 101), g(173 : 46), b(249 : 106) Diff 192 
bool(true)
Comp r(121 : 121), g(173 : 173), b(249 : 249) Diff 0 
bool(true)
Comp r(121 : 219), g(173 : 179), b(249 : 268) Diff 100 
bool(true)
Comp r(219 : 101), g(179 : 46), b(268 : 106) Diff 241 
bool(true)
Comp r(219 : 121), g(179 : 173), b(268 : 249) Diff 100 
bool(true)
Comp r(219 : 219), g(179 : 179), b(268 : 268) Diff 0 
bool(true)
Different
Comp r(101 : 446), g(46 : 865), b(106 : 1242) Diff 1442 
bool(false)
Comp r(121 : 446), g(173 : 865), b(249 : 1242) Diff 1253 
bool(false)
Comp r(219 : 446), g(179 : 865), b(268 : 1242) Diff 1213 
bool(false)
Comp r(121 : 446), g(173 : 865), b(249 : 1242) Diff 1253 
bool(false)
Comp r(121 : 654), g(173 : 768), b(249 : 1180) Diff 1227 
bool(false)
Comp r(121 : 708), g(173 : 748), b(249 : 1059) Diff 1154 
bool(false)
Comp r(219 : 446), g(179 : 865), b(268 : 1242) Diff 1213 
bool(false)
Comp r(219 : 654), g(179 : 768), b(268 : 1180) Diff 1170 
bool(false)
Comp r(219 : 708), g(179 : 748), b(268 : 1059) Diff 1090 
bool(false)

In this calculation the background is ignored what leads to bigger difference in the avg color.

Final implementation (OOP)

Quite interessting topic. So i tryed to tune it up a liddle bit. This is now a complete OOP implementation. You can now create a new image and subtract some mask of it in order to eliminate a background. Then you can compare one image to another using the compare method. To keep the calculation limited it's better to resize your image first (masks are allways fittet to the current image)

The compare algorythme it self chunks the two images into serveral tiles, then eliminates tiles, that are almost equal to white average color and then compares the average color of all remaining tile-permutations.

Class Image {

    const HASH_SIZE = 8;
    const AVG_SIZE = 10;

    private $img = null;

    public function __construct($resource)
    {
        $this->img = $resource;;
    }

    private function permute(array $a1, array $a2) {
        $perms = array();
        for($i = 0; $i < sizeof($a1); $i++) {
            for($j = $i; $j < sizeof($a2); $j++) {
                if ($i != $j) {
                    $perms[] = [$a1[$i], 
                    $a2[$j]];
                }
            }
        }

        return $perms;
    }

    public function compare(Image $comp) {
        $avgComp = array();

        foreach($comp->chunk(25) as $chunk) {
            $avgComp[] = $chunk->avg();
        }

        $avgOrg = array();

        foreach($this->chunk(25) as $chunk) {
            $avgOrg[] = $chunk->avg();
        }

        $white = Color::fromInt(0xFFFFFF);

        $avgComp = array_values(array_filter($avgComp, function(Color $color) use ($white){
            return $white->compare($color, 1000);
        }));

        $avgOrg = array_values(array_filter($avgOrg, function(Color $color) use ($white){
            return $white->compare($color, 1000);
        }));

        $equal = 0;
        $pairs = $this->permute($avgOrg, $avgComp);

        foreach($pairs as $pair) {
            $equal += $pair[0]->compare($pair[1], 100) ? 1 : 0;
        }

        return ($equal / sizeof($pairs));
    }

    public function substract(Image $mask, $tolerance = 50)
    {
        $size = $this->size();

        if ($mask->size() != $size) {
            $mask = $mask->resize($size);
        }

        for ($x = 0; $x < $size[0]; $x++) {
            for ($y = 0; $y < $size[1]; $y++) {
                if ($this->colorat($x, $y)->compare($mask->colorat($x, $y), $tolerance))
                    imagesetpixel($this->img, $x, $y, 0xFFFFFF);
            }
        }

        return $this;
    }

    public function avg($size = 10)
    {
        $target = $this->resize([self::AVG_SIZE, self::AVG_SIZE]);

        $avg   = Color::fromInt(0x000000);
        $white = Color::fromInt(0xFFFFFF);  

        for ($x = 0; $x < self::AVG_SIZE; $x++) {
            for ($y = 0; $y < self::AVG_SIZE; $y++) {
                $color = $target->colorat($x, $y);
                if (!$color->compare($white, 10))
                    $avg->mix($color);
            }
        }

        return $avg;
    }

    public function colorat($x, $y)
    {
        return Color::fromInt(imagecolorat($this->img, $x, $y));
    }

    public function chunk($chunkSize = 10)
    {
        $collection = new ImageCollection();
        $size = $this->size();

        for($x = 0; $x < $size[0]; $x += $chunkSize) {
            for($y = 0; $y < $size[1]; $y += $chunkSize) {
                switch (true) {
                    case ($x + $chunkSize > $size[0] && $y + $chunkSize > $size[1]):
                        $collection->push($this->slice(['x' => $x, 'y' => $y, 'height' => $size[0] - $x, 'width' => $size[1] - $y]));
                        break;
                    case ($x + $chunkSize > $size[0]):
                        $collection->push($this->slice(['x' => $x, 'y' => $y, 'height' => $size[0] - $x, 'width' => $chunkSize]));
                        break;
                    case ($y + $chunkSize > $size[1]):
                        $collection->push($this->slice(['x' => $x, 'y' => $y, 'height' => $chunkSize, 'width' => $size[1] - $y]));
                        break;
                    default:
                        $collection->push($this->slice(['x' => $x, 'y' => $y, 'height' => $chunkSize, 'width' => $chunkSize]));
                        break;
                }
            }
        }

        return $collection;
    }

    public function slice(array $rect)
    {
        return Image::fromResource(imagecrop($this->img, $rect));
    }

    public function size()
    {
        return [imagesx($this->img), imagesy($this->img)];
    }

    public function resize(array $size = array(100, 100))
    {
        $target = imagecreatetruecolor($size[0], $size[1]);
        imagecopyresized($target, $this->img, 0, 0, 0, 0, $size[0], $size[1], imagesx($this->img), imagesy($this->img));

        return Image::fromResource($target);
    }

    public function show()
    {
        header("Content-type: image/png");
        imagepng($this->img);
        die();
    }

    public function save($name = null, $path = '') {
        if ($name === null) {
            $name = $this->hash();
        }

        imagepng($this->img, $path . $name . '.png');

        return $this;
    }

    public function hash()
    {
                // Resize the image.
        $resized = imagecreatetruecolor(self::HASH_SIZE, self::HASH_SIZE);
        imagecopyresampled($resized, $this->img, 0, 0, 0, 0, self::HASH_SIZE, self::HASH_SIZE, imagesx($this->img), imagesy($this->img));
        // Create an array of greyscale pixel values.
        $pixels = [];
        for ($y = 0; $y < self::HASH_SIZE; $y++)
        {
            for ($x = 0; $x < self::HASH_SIZE; $x++)
            {
                $rgb = imagecolorsforindex($resized, imagecolorat($resized, $x, $y));
                $pixels[] = floor(($rgb['red'] + $rgb['green'] + $rgb['blue']) / 3);
            }
        }
        // Free up memory.
        imagedestroy($resized);
        // Get the average pixel value.
        $average = floor(array_sum($pixels) / count($pixels));
        // Each hash bit is set based on whether the current pixels value is above or below the average.
        $hash = 0; $one = 1;
        foreach ($pixels as $pixel)
        {
            if ($pixel > $average) $hash |= $one;
            $one = $one << 1;
        }
        return $hash;
    }

    public static function fromResource($resource)
    {
        return new self($resource);
    }

    public static function fromBin($binf)
    {
        return new self(imagecreatefromstring($bin));
    }

    public static function fromFile($path)
    {
        return new self(imagecreatefromstring(file_get_contents($path)));
    }
}

class ImageCollection implements IteratorAggregate
{
    private $images = array();

    public function __construct(array $images = array())
    {
        $this->images = $images;
    }

    public function push(Image $image) {
        $this->images[] = $image;
        return $this;
    }

    public function pop()
    {
        return array_pop($this->images);
    }

    public function save()
    {
        foreach($this->images as $image)
        {
            $image->save();
        }

        return $this;
    }

    public function getIterator() {
        return new ArrayIterator($this->images);
    }
}

class Color {
    private $r = 0;
    private $g = 0;
    private $b = 0;

    public function __construct($r = 0, $g = 0, $b = 0)
    {
        $this->r = $r;
        $this->g = $g;
        $this->b = $b;
    }

    public function r()
    {
        return $this->r;
    }

    public function g()
    {
        return $this->g;
    }

    public function b()
    {
        return $this->b;
    }

    public function toInt()
    {
        return $this->r << 16 + $this->g << 8 + $this->b;
    }

    public function toRgb()
    {
        return [$this->r, $this->g, $this->b];  
    }

    public function mix(Color $color)
    {
        $this->r = round($this->r + $color->r() / 2);
        $this->g = round($this->g + $color->g() / 2);
        $this->b = round($this->b + $color->b() / 2);
    }

    public function compare(Color $color, $tolerance = 500)
    {
        list($r1, $g1, $b1) = $this->toRgb();
        list($r2, $g2, $b2) = $color->toRgb();

        $diff = round(sqrt(pow($r1 - $r2, 2) + pow($g1 - $g2, 2) + pow($b1 - $b2, 2)));

        //printf("Comp r(%s : %s), g(%s : %s), b(%s : %s) Diff %s \n", $r1, $r2, $g1, $g2, $b1, $b2, $diff);

        return  $diff <= $tolerance;
    }

    public static function fromInt($int) {
        return new self($int >> 16, $int >> 8 & 255, $int & 255);
    }
}

$mask = Image::fromFile('http://i.stack.imgur.com/gfn5A.png');

$image1 = Image::fromFile('http://i.stack.imgur.com/D8ct1.png')->resize([50, 100])->substract($mask, 100);
$image2 = Image::fromFile('http://i.stack.imgur.com/xNZt1.png')->resize([50, 100])->substract($mask, 100);
$image3 = Image::fromFile('http://i.stack.imgur.com/kjGjm.png')->resize([50, 100])->substract($mask, 100);

$other1 = Image::fromFile('http://i.stack.imgur.com/WIOHs.png')->resize([50, 100])->substract($mask, 100);
$other2 = Image::fromFile('http://i.stack.imgur.com/ljoBT.png')->resize([50, 100])->substract($mask, 100);
$other3 = Image::fromFile('http://i.stack.imgur.com/qEKSK.png')->resize([50, 100])->substract($mask, 100);


echo "Equal\n";
var_dump(
    $image1->compare($image2),
    $image1->compare($image3),
    $image2->compare($image3)
);

echo "Image 1 to Other\n";
var_dump(
    $image1->compare($other1),
    $image1->compare($other2),
    $image1->compare($other3)
);

echo "Image 2 to Other\n";
var_dump(
    $image2->compare($other1),
    $image2->compare($other2),
    $image2->compare($other3)
);

echo "Image 3 to Other\n";
var_dump(
    $image3->compare($other1),
    $image3->compare($other2),
    $image3->compare($other3)
);

Result:

Equal
float(0.47619047619048)
float(0.53333333333333)
float(0.4)
Image 1 to Other
int(0)
int(0)
int(0)
Image 2 to Other
int(0)
int(0)
int(0)
Image 3 to Other
int(0)
int(0)
int(0)

Try the SIFT or SURF features. They are scale, rotation and translation invariant. Run k-means on the descriptors and go from feature vectors to integer codes  Acording to my requirements I want to identify all of these images as similar, since it has use the same color, same clip art. The only difference in these images are rotation ,scale and the placement of the clip art. Since all 3 t-shirts has used the same color and clip art I want to identify all 3 images as similar.

This tool computes (dis)similarity between two or more PNG images using an algorithm I made some research on image similarity comparison these days. These methods can match images under different scales, rotations, and lighting. a metric used to measure how similar the documents are irrespective of their size. This is the main function which will compare image similarity by using all the above functions and provide the image comparison value. If the image is less than or lessthan equal to 10 it means the image is almost similar otherwise image is not duplicate.

SIMILAR computes the normalized cross correlation similarity metric between two equal dimensioned images. The normalized cross correlation metric measures how similar two images are, not how different they are.The range of ncc metric values is between 0 (dissimilar) and 1 (similar). If mode=g, then the two images will be converted to grayscale. If mode=rgb, then the two images first will be converted to colorspace=rgb. Next, the ncc similarity metric will be computed for each channel. Finally, they will be combined into an rms value. NOTE: this metric does not work for constant color channels as it produces an ncc metric = 0/0 for that channel. Thus it is not advised to run the script with either image having a totally opaque or totally transparent alpha channel that is enabled.

try this api,

http://www.phpclasses.org/package/8255-PHP-Compare-two-images-to-find-if-they-are-similar.html

Similarity is concerned with entities related by virtues of their likeness, e.g., SIFT descriptors are selected for their invariance to image scale, rotation, Compared to other image databases with keyword annotations, we believe In the second scenario, we measure the relatedness of words and images regardless of the. Some applications need to able to tell if two images are similar in the sense that they show the same thing. The PHP Compare Images Similarity class can be used for that purpose. Read this article to learn about this class and how you can use it in PHP applications for checking if two images are the same or not.

As someone mentioned, anything else than calculating the histogram of the images and comparing them is not easily achievable. Here is an example that gives the correct result for the images provided in question. The key point here is how to get the right balance between the number of peak color levels and what is acceptable amount of them ( similarity( $histograms, $levels = 30, $enough = 28 ) ).

function histograms( $images ) {
    foreach( $images as $img ) {
        $image = imagecreatefrompng( $img );
        $width = imagesx( $image );
        $height = imagesy( $image );
        $num_pixels = $width * $height; 

        $histogram = [];
        for ( $x = 0; $x < $width; $x++ ) {
            for ( $y = 0; $y < $height; $y++ ) {
                $rgb = imagecolorat( $image, $y, $x );
                $rgb = [ $rgb >> 16, ( $rgb >> 8 ) & 0xFF, $rgb & 0xFF ];

                $histo_v = (int) round( ( $rgb[0] + $rgb[1] + $rgb[02] ) / 3 );
                $histogram[ $histo_v ] = array_key_exists( $histo_v, $histogram ) ? $histogram[ $histo_v ] + $histo_v/$num_pixels : $histo_v/$num_pixels;
            }
        }
        $histograms[$img] = $histogram;
        arsort( $histograms[$img] );
    }

    return $histograms;
}


function similarity( $histograms, $levels = 30, $enough = 28 ) {
    $keys = array_keys( $histograms );
    $output = [];
    for ( $x = 0; $x < count( $histograms ) - 1; $x++ ) {
        for ( $y = $x + 1; $y < count( $histograms ); $y++ ) {      
            $similarity = count( array_intersect_key( array_slice( $histograms[ $keys[$x] ], 0, $levels, true ), array_slice( $histograms[ $keys[$y] ], 0, $levels, true ) ) );

            if ( $similarity > $enough ) $output[] = [ $keys[$x], $keys[$y], $similarity ];                 
        }
    }
    return $output;
}


$histograms = histograms( [ 'http://i.stack.imgur.com/D8ct1.png', 'http://i.stack.imgur.com/xNZt1.png', 'http://i.stack.imgur.com/kjGjm.png', 'http://i.stack.imgur.com/WIOHs.png', 'http://i.stack.imgur.com/ljoBT.png', 'http://i.stack.imgur.com/qEKSK.png' ] );
$similarity = similarity( $histograms );

print_r( $similarity );

/*
Array
(
    [0] => Array
        (
            [0] => http://i.stack.imgur.com/D8ct1.png
            [1] => http://i.stack.imgur.com/xNZt1.png
            [2] => 30
        )

    [1] => Array
        (
            [0] => http://i.stack.imgur.com/D8ct1.png
            [1] => http://i.stack.imgur.com/kjGjm.png
            [2] => 30
        )

    [2] => Array
        (
            [0] => http://i.stack.imgur.com/D8ct1.png
            [1] => http://i.stack.imgur.com/qEKSK.png
            [2] => 29
        )

    [3] => Array
        (
            [0] => http://i.stack.imgur.com/xNZt1.png
            [1] => http://i.stack.imgur.com/kjGjm.png
            [2] => 30
        )

    [4] => Array
        (
            [0] => http://i.stack.imgur.com/xNZt1.png
            [1] => http://i.stack.imgur.com/qEKSK.png
            [2] => 29
        )

    [5] => Array
        (
            [0] => http://i.stack.imgur.com/kjGjm.png
            [1] => http://i.stack.imgur.com/qEKSK.png
            [2] => 29
        )

)
*/

This article also helped me to create the histograms.

In contrast, perceptual hashes can be compared -- giving you a sense of similarity between the two data sets. This way, the hash will match any variation of the image, regardless of scale or aspect ratio. Reduce color. The tiny http://www.hackerfactor.com/blog/index.php?/archives/252-PNG-and-Cameras.​html. Basically  This class can compare two images to find if they are similar. It resizes the images to a 8x8 square, converts the result into grey scale images, takes the color mean, and computes the hammering distance of the images' bits. The class returns a number that is below 10 if the images are similar.

only translated, but also rotated and with the change of scale, the Fourier-Mellin transform was used. Use of the image discrete transforms is original in this field, Tracking changes – comparison of images of the same object obtained at Fourier-Mellin transform for detecting watermark in images regardless of the scaling. Image Similarity using Deep Ranking This paper gives a really good explanation and a comparison between Manhattan and Euclidean distance in the context of image similarity. Their results

Website: www.sciencepubco.com/index.php/IJET scale and angle invariant during the image recognition process where the template matching using normalized cross correlation to measure similarity Under these assumptions, this paper considers comparing two combinations of sizes and rotation is carried out. SIFT proposed by Lowe solves the image rotation, affine transformations, intensity, and viewpoint change in matching features. The SIFT algorithm has 4 basic steps. First is to estimate a scale space extrema using the Difference of Gaussian (DoG). Secondly, a key point localization where the

Questions and postings pertaining to the usage of ImageMagick regardless of the interface. I work in biotech, and we use a liquid handler to run assays for us. be used for two different size images to find translation, but not rotation or scale difference. http://www.imagemagick.org/script/compare.php The ability to find include a similar set of images for a given image has multiple uses-cases from visual search CNNto duplicate product detection to domain specific image clustering. Our approach called SimNet, tries a to identify similar images for a new image using multi-scale Siamese network. Fig. 1 shows examples of image samples from

Comments
  • Does the illumination vary in the images? Because you want "rotatation-" and "scale-invariance" a robust image comparison is not easily achievable. stackoverflow.com/questions/843972/…. Given the example image-set you provided, I suggest calculating the histogram of the images and comparing them. The hard part is finding the right threshold..
  • @bro illumination will not be changed. Any way thanks for your suggestion.I will try that
  • @bro In the link you provided it says that re-scaled, rotated, or discolored images can fail with the histogram method. So, I think histogram will not work here. Do you have any suggestions?
  • check here stackoverflow.com/questions/3270929/compare-2-images-in-php
  • @kraysak it didn't give me the solution.
  • Calculating color value is not a good solution. I get wrong results with this code.
  • @Tharu I made a second approach, that seems to work better.
  • When tries to run your above code, it gives me an error saying "Call to undefined function imagecrop()" inside the slice() function. Do you have any idea on how to fix that?
  • How do you measure the equality of the 2 images? I mean if the result get '0' is it similar? How do you measure the similarity?
  • @Tharu I moved the thing to GitHub tryout github.com/inceddy/ImageCompare
  • Calculating color value is not a good solution. I get wrong results. According to the sample pictures I have provided, I want the very first image(T shirt with only one clip art) and the last image(T shirt with same clip art but twice) to be identified as 2 different ones. But the link you provided, calculates the mean difference of the color, It gives the result for the mentioned images as similar. But that is not I want.What I want is something like obeject-to-object comparison
  • For the very 1st image(image with one clipart) I have provided and the last image(image with 2 cliparts) I got the value as 30. That means those are similar. But I want to identify those as dissimilar. I hope you got what i'm saying