This document discusses how to build a search by color functionality. It explains that images can be represented as numeric color data and indexed in Solr. Color histograms or aggregate statistics can be indexed for each image. Queries can search on specific colors or ranges. Relevance can be improved by scoring images based on the percentage of pixels that match colors, proximity of colors, and accounting for similar colors. More advanced techniques like object detection could further enhance image search.

2. Searching Images by Color
Chris Becker
Search Engineering @ Shutterstock

3. What is Shutterstock?!
• Shutterstock sells stock images, videos & music.!
• Crowdsourced from artists around the world!
• Shutterstock reviews and indexes them for search!
• Customers by a subscription and download them!

4. Why search by color?!

5. Stock photography on the internet…!

6. Stock photography on the internet…!

7. Color is one of several visual
attributes that you can use !
to create an engaging !
image search experience!

8. Shutterstock Labs!
www.shutterstock.com/labs!
!
Spectrum!
Palette!

9. Diving into Color Data!

10. Color Spaces!
• RGB!
!
• HSL!
!
• LCH!
!
• Lab!

11. Calculating Distances Between Colors!
• Euclidean distance works reasonably well in any color
space!
!
distRGB = sqrt((r1-r2)^2 + (g1-g2)^2 + (b1-b2)^2)!
distHSL = sqrt((h1-h2)^2 + (s1-s2)^2 + (l1-l2)^2)!
distLCH = sqrt((L1-L2)^2 + (C1-C2)^2 + (H1-H2)^2)!
!
• More sophisticated equations that better account for
human perception can be found at!
http://en.wikipedia.org/wiki/Color_difference!
!

12. Images are just numbers!
[
[[054,087,058], [054,116,206], [017,226,194], [234,203,215], [188,205,000], [229,156,182]],
[[214,238,109], [064,190,104], [191,024,161], [104,071,036], [222,081,005], [204,012,113]],
[[197,100,189], [159,204,024], [228,214,054], [250,098,125], [050,144,093], [021,122,101]],
[[255,146,010], [115,156,002], [174,023,137], [161,141,077], [154,189,005], [242,170,074]],
[[113,146,064], [196,057,200], [123,203,160], [066,090,234], [200,186,103], [099,074,037]],
[[194,022,018], [226,045,008], [123,023,087], [171,029,021], [040,001,143], [255,083,194]],
[[115,186,246], [025,064,109], [029,071,001], [140,031,002], [248,170,244], [134,112,252]],
[[116,179,059], [217,205,159], [157,060,251], [151,205,058], [036,214,075], [107,103,130]],
[[052,003,227], [184,037,078], [161,155,181], [051,070,186], [082,235,108], [129,233,211]],
[[047,212,209], [250,236,085], [038,128,148], [115,171,113], [186,092,227], [198,130,024]],
[[225,210,064], [123,049,199], [173,207,164], [161,069,220], [002,228,184], [170,248,075]],
[[234,157,201], [168,027,113], [117,080,236], [168,131,247], [028,177,060], [187,147,084]],
[[184,166,096], [107,117,037], [154,208,093], [237,090,188], [007,076,086], [224,239,210]],
[[105,230,058], [002,122,240], [036,151,107], [101,023,149], [048,010,225], [109,102,195]],
[[050,019,169], [219,235,027], [061,064,133], [218,221,113], [009,032,125], [109,151,137]],
[[010,037,189], [216,010,101], [000,037,084], [166,225,127], [203,067,214], [110,020,245]],
[[180,147,130], [045,251,177], [127,175,215], [237,161,084], [208,027,218], [244,194,034]],
[[089,235,226], [106,219,220], [010,040,006], [094,138,058], [148,081,166], [249,216,177]],
[[121,110,034], [007,232,255], [214,052,035], [086,100,020], [191,064,105], [129,254,207]],
]

13. Any operation you can do on a set of
numbers, you can do on an image!
• getting histograms!
• computing median values!
• standard deviations / variance!
• other statistics !

15. Extracting Color Data!

16. Tools & Libraries!
• ImageMagick!
• Python Image Library!
• ImageJ!

17. Code Example!
#! /usr/bin/env perl!
use Image::Magick;!
!
my $image = Image::Magick->new;!
$image->Read(‘SamplePhoto.jpg’);!
$image->Quantize(colorspace => 'RGB', colors => 64);!
my @histogram = $image->Histogram();!
my %colors;!
!
while ( my($R,$G,$B,$opacity,$count) = splice(@histogram,0,5)) {!
!
# convert r,g,b to a hex color value!
my $hex = sprintf("%02x%02x%02x",!
$R / 256,!
$G / 256,!
$B / 256!
);!
!
$colors{$hex} += $count; !
}!

18. Indexing & Searching
in Solr!

19. Indexing color histograms!
• index colors just like you would index text!
• volume of color == frequency of the term!
color_txt = "cfebc2
cfebc2 cfebc2 cfebc2
cfebc2 2e6b2e 2e6b2e
2e6b2e ff0000 …"

20. Solr Fields & Queries!
<field name="color" type="text_ws" …>!
• Easy to query!
• Can use solr’s default ranking effectively!
!
/solr/select?q=ff0000 e2c2d2&qf=color&defType=edismax…!
!
• or access term frequencies directly to create specific sort
functions:!
!
sort=product(tf(color,"ff0000"),tf(color,"e2c2d2")) desc!

21. Indexing color statistics!
Represent aggregate statistics of each image!
lightness:
median: 2
standard dev: 1
largest bin: 0
largest bin size: 50
saturation
median: 0
standard dev: 0
largest bin: 0
largest bin size: 100
…

22. Solr Fields & Queries!
<field name=”hue_median” type=”int” …>!
• Sort by the distance between input param and median value!
!
/solr/select?q=*&sort=abs(sub($query,hue_median)) asc!

23. Ranking & Relevance!

24. How much of the image has the color ? !

25. is this relevant if I search for ?!

26. which image is more relevant if I search for ?!

27. is this relevant if I search for ?!

28. How do we account for these factors?!

29. How much of the image contains the
selected color?!
• Score each color by number/percentage of pixels!
!
sort=tf(color,"ff9900") desc!

30. Color Accuracy!
• As you reduce your color space, you also reduce
precision!
• reducing the colorspace too much increases recall and lowers precision. !
• Not reducing it enough lowers recall and higher precision.!
• reducing your color space down to ~100 to ~300 colors works well!

31. Weighing Multiple Colors Equally!
• If you search for 2 or more colors, the top result should
have the most even distribution of those colors!
• simple option:!
!
sort=product(tf(color,"ff9900"),tf(color,"2280e2")) desc!
!
• more complex: compute the stdev or variance of the
matching color values in your solr sort function, and sort the
results with the lowest variance first. !
!

32. Accounting for Similar & Different
Colors!
• The score for a particular color should reflect all the colors in the image.!
• At indexing time, increase the score based on similar colors; decrease it
based on differing colors.!

33. Conclusion!

34. Conclusion!
• This talk provided a rough guide to building a basic search-by-color
application!
• Lots of opportunity to do more sophisticated things in image search. !
• matching colors in certain parts of an image!
• identifying visual styles (blur vs sharp, high contrast, etc)!
• patterns & textures!
• analyzing content in images (object detection)!
!
!

35. One more demo…!