java Fingerprint + Google image recognition technology
Recently saw this " principle similar image search " blog on Ruan Yifeng 's blog , there is an impulse to want to achieve out of these principles .
wrote a demo of picture identification
provide source code download , source code download link: http://download.csdn.net/detail/yjflinchong/4232770
to try the effect of it
Google " Similar Image Search " : You can use an image search on the Internet for all its similar images .
Open Google Image Search page :
Click to upload a angelababy original use :
After clicking search , Google will identify with similar images , pictures more similarity higher top surface . Such as:
What is the principle of this technique ? Computer how to know two pictures similar to it ?
According to Dr. Neal Krawetz explanation to achieve similar image search key technology elements called " perceptual hash algorithm " (Perceptualhash algorithm), its role is to generate a "fingerprint" for each picture (fingerprint) string, and then compare the different pictures of fingerprints. The closer the results , it shows more similar pictures .
The following is a simple Java implementation:
pretreatment : Reading Pictures
First, reduce the size .
pictures down to 8x8 size, a total of 64 pixels. This step is to remove the role of the details of the picture , leaving only the structure , shading , and other basic information , get rid of different sizes , the proportion of the differences brought pictures .
The second step, to simplify color .
will be reduced after the picture turned 64 shades of gray. That is, all pixels total of only 64 colors .
The third step is to calculate the average .
calculate the average of all 64 gray pixels .
fourth step, comparing pixel gray .
The gradation of each pixel , and compares the average value . Greater than or equal to the average , denoted by 1 ; less than the average , denoted by 0 .
fifth step, compute the hash value.
The results of the comparison step , together, constitute a 64- bit integer , which is this picture of a fingerprint . Mix order is not important , as long as the guarantee that all pictures are used in the same order on the line.
After
get fingerprints , you can compare different pictures and see how many 64 -bit is not the same . In theory , this is equivalent to calculating " Hamming distance " (Hammingdistance). If not the same data bit is not more than 5 , it shows two pictures are very similar ; If greater than 10, it stated that this is two different pictures .
You can put together a few pictures , but also to calculate the contrast of their Hamming distance , you can see two images are similar.
advantage of this algorithm is simple and fast , free pictures size scaling effects , the disadvantage is the content of the image can not be changed . If you add a few words on the picture , it will not recognize it . So , it 's best use is based on the thumbnails to find artwork .
practical applications , often using more powerful algorithms and pHash SIFT algorithm , they are able to identify the deformation of the picture. As long as no more than 25% degree of deformation , which can match the original . Although these algorithms more complicated , but the principle is simple with the above algorithm is the same , that is, the first image into Hash string , and then compared.
Most of the above Ruan Yifeng copied directly from the site over, want to see the original shoes can go click on the link at the top to go see .
provide source code download , source code download link: http://download.csdn.net/detail/yjflinchong/4232770
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I tried. operating results came out. for your reference
Resources:
[f4c4c0c0f0888c07, ffff008cfcfcfcff, 7f7f47037f7f7f74, 7f7f004f7f7f7c7f, 7f7f004f7f7f7c7f, 7f7f004f7f7f007f]
Source:
7f7f004f7f7f7c7f
Hamming distance : 14 source.jpg picture is completely different now example1.jpg
Hamming distance : 10 source.jpg picture is somewhat similar with example2.jpg
Hamming distance : 6 source.jpg picture is somewhat similar with example3.jpg
Hamming distance : 0 source.jpg the same picture with example4.jpg
Hamming distance : 0 source.jpg the same picture with example5.jpg
Hamming distance : 2 source.jpg very similar picture with example6.jpg
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effect can be it .
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is a bit greedy . However, the code has . Notes there . test image , too. demo has
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test print :
Resources:
[f4c4c0c0f0888c07, ffff008cfcfcfcff, 7f7f47037f7f7f74, 7f7f004f7f7f7c7f, 7f7f004f7f7f7c7f, 7f7f004f7f7f007f]
Source:
7f7f004f7f7f7c7f
Hamming distance : 14 source.jpg picture is completely different now example1.jpg
Hamming distance : 10 source.jpg picture is somewhat similar with example2.jpg
Hamming distance : 6 source.jpg picture is somewhat similar with example3.jpg
Hamming distance : 0 source.jpg the same picture with example4.jpg
Hamming distance : 0 source.jpg the same picture with example5.jpg
Hamming distance : 2 source.jpg pictures with example6.jpg very similar
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perceptual hashing algorithm
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because of the recent things more. To the various codes are really more than a friend . If not waited in vain , if tainted hair , you can spend points to download :
http://download.csdn.net/detail/yjflinchong/4232770
Thank you.
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