imada presentation

Martin R. [email protected]

mailto:[email protected]



Outline• Personal introduction

• What is Colourbox?

• Why is Colourbox interesting?

• Similar images

• Search result ranking

• Recommendations

• Why Colourbox?

• Open position

• Questions




Who am I?Why am I here?

• Me

• Graduated from IMADA, 2010

• Ph.D. in Computer Science

• Online Algorithms

• Technical Project Manager& System Architect

• Why this talk?

• Promote Colourbox

• There are interesting jobs on Funen




Colourbox• Microstock photography company

• Resell images, vector graphics, videos

• March 2006

• 3 employees, 50 users, 50,000 images,150 new images daily

• November 2011

• 21 employees, 65,000 users, 2,000,000 images,5,000 new images daily

• Currently in top 10 of all stock sites, aiming at #1




Colourbox• Only stock site that offers

flat rate

• Download all you want for €249,- per month

• Search, find, download

• Browse, get inspired, download



http://www.colourbox.com

http://www.colourbox.com


The Tech• Build using open source software

• HTML(5), CSS(3), and Javascript (jQuery) front-end

• Varnish, Lighttpd, and Memcached

• MySQL (Percona) database

• PHP backend

• PHP, Python, and C++ scripts

• Self-developed search engine (Colourit)

• Using Python and C

• Cloud based on Amazon EC2 and S3




The Setup




The Geek Side• Techniques from mathematics and computer science• Distributed/parallel computing

• Vector mathematics• Various tree structures

• Set intersection

• Cache oblivious algorithms• Clustering algorithms

• Ranking algorithms• Markov chains

• etc...




Similar images• Given an image, what other images look similar to it?

• Inspire

• Browse

• All images have keywords

• The keyword-to-image association is weighted

• How pronounced is the keyword for the image?

• Calculated automatically (more later)




Similar images




Similar images• Each keyword is a dimension in keyword vector space

• Each image is then represented as a vector in this space

• The projection onto each dimension is the weight of the corresponding keyword

• Example

• (goat, 96), (white, 94), (outside, 50)

• Vector (x, y, z, w) = (0.96, 0.94, 0.5, 0)

• (goat, 47), (white, 81), (day, 19)

• Vector (x, y, z, w) = (0.47, 0.81, 0, 0.19)




Similar images• Similarity is then the angle between two vectors

• Easily calculated using high school math

• Result between 0 and 90 degrees

• Example (cont.)

• (0.96, 0.94, 0.5, 0) and (0.47, 0.81, 0, 0.19)

• Approx 27.73 degrees

• Do two images with similarity of 27.73 degrees look similar?

• Experiments determined the cut-off

�u · �v = cos(θ)|�u||�v|




Similar images




Similar images• 2,000,000 images yields 2,000,000,000,000 comparisons

• No job dependencies

• No data modifications

• Relatively small data size

• Each keyword is identified by a number

• Very easy to do in parallel and distribute

• Speed up using a trick from cache oblivious algorithms

• This is not a one-time thing

• Keywords and weights change




Ranking of results• How to rank search results?

• Want the “best” results first

• First solution: Use number of downloads as parameter

• Problems

• Old good images rank over new excellent images

• Wrong keywords distort the results




Ranking of results• Harvest information from the users

• A clicked/downloaded image

• Matched the search string well

• Is a “good” image

• A shown-but-not-clicked image either

• Does not match the search string well, or

• Is a “bad” image




Ranking of results• The keyword-to-image association is weighted

• Keyword weights are updated when

• a keyworder assigns a keyword (high weight)

• a supplier assigns a keyword (high weight)

• a user clicks on a photo presented by a search

• a user does NOT click on a photo presented




Ranking of results• Search “Summer Lemon”

• User clicks first result

• Pros

• Second image rankedlower for “Lemon”

• Cons

• “Summer” ranked loweron second image

• Fixed by subsequentsearches

Lemon (0.7)Summer (0.9)Apple (0.0)







Ranking of results• Images with

• Wrong keywords are ranked very low over time

• Good keywords are ranked higher

• Great images are ranked higher overall

• New excellent images can rank over old mediocre images




Recommendations• “You are currently looking at image X,

and you might be interested in image Y, Z, and W”




Recommendations• What images are connected?

• Let’s track our users to find out




Recommendations

#2364906 #2964241 #2684393




Recommendations




Recommendations• Enter Markov chains

• Using a Markov chain of order 1, the probability of going from media X to media Y is

• How many times path X -> Y was followed, divided by

• Sum over all paths going out of image X




Why Colourbox?• We are

• small - 15 people no more than 15 steps apart

• flat - no long chains of command• flexible - we can move on good idea immediately

• a 2011 Gazelle - we are still hiring while others are still firing

• We have• Relaxed atmosphere

• Flexible work hours

• Candy cabinet, world class coffee machine, and stunning view :-)

• etc...




Why Colourbox?• You get

• to work on fun problems

• great colleagues

• an international outlook

• to serve customers who are excited about us

• to be part of a company which aims to be #1

• New projects

• SkyFish - Company Colourbox

• Zulubox - to articles what Colourbox is to images




We are hiring!• Software Developer – front-end systems

• Focus on HTML5, JS, PHP, SQL, etc.

• Can implement a pixel-perfect design from a PSD

• Can implement scalable code that also performs well when it is executed 50 times per second

• You know your way around Linux

• Start August 1st

• We are construction a new office building

• Unsolicited applications are always welcome




Thank you!

Questions?



imada presentation

Technology