java fingerprint + Google image recognition technology
while back in Ruan Yifeng 's blog saw this " Similar Image Search Theory" blog , there is an impulse to implement these principles out.
wrote a demo 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 with all its similar images .
Open Google Image Search page :
Click to use the Upload a angelababy picture :
click on the search , Google will identify with similar images , pictures more the higher the similarity the 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 interpretation similar picture search elements to achieve key technology called "perception hash algorithm " (Perceptualhash algorithm), it is the role for each picture to generate a "fingerprint" (fingerprint) string, and then compare the different pictures of fingerprints. Results closer , it shows more similar images .
The following is a simple Java implementation :
pretreatment : Read Image
first step, the reduced size .
the picture down to the size of 8x8 , a total of 64 pixels. The role of this step is to remove the picture of the details , leaving only the structure , shading and other basic information , get rid of different size, scale images to bring the difference .
The second step, simplified colors.
will shrink after the picture , turn 64 shades of gray. That is, all pixels of a total of only 64 colors .
The third step is to calculate the average.
calculated for all 64 pixel gray average.
fourth step, comparing pixel gray .
gray level of each pixel , compared with 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.
comparison of the previous step , together, to form a 64-bit integer , which is the fingerprint of this picture . Mix order is not important, as long as the guarantee that all pictures are using the same order on the line.
get fingerprints later, you can compare different pictures to see 64 how many bits are not the same. In theory, this is equivalent to computing " 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 pictures similar .
advantage of this algorithm is simple and quick , without scaling of image size , the disadvantage is the picture content is not 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 can identify the picture distortion. As long as the deformation does not exceed 25% , they can match the original . Although these algorithms are more complicated , but the principle is simple with the above algorithm is the same as the first picture is transformed into Hash string , and then compared.
Most of the content above website directly from Ruan Yifeng copied , 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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to see
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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 with 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 the example4.jpg
Hamming distance : 0 source.jpg the same picture with the example5.jpg
Hamming distance : 2 source.jpg picture is very similar with example6.jpg
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effect can also be yet.
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Reply Mail a friend
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is a bit greedy . However code . Comments have . test images 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 with 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 the example4.jpg
Hamming distance : 0 source.jpg the same picture with the example5.jpg
Hamming distance : 2 source.jpg pictures with example6.jpg very similar
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perception hash algorithm
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because of the recent things more. To various codes of friends too much. If you do 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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