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Fashion product de-duplication with image similarity and LSH

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Eddie Bell

My talk from pydata London 12/2014

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Matching Fashion Products With LSH Image Similarity
Hi, I’m Eddie. @ejbell
We collect the world of fashion into a customisable shopping experience. 3
What makes us different? All data is scraped from retailers 500 spiders (scrapy), 9000 designers Almost everything is automated SEO, recommendation, classification, sales This architecture comes with a few problems
Why do we get duplicates? There is no ISBN for fashion Burberry Selfridges inter-retailer intra-retailer intra-retailer
How We Used to Find Duplicates Lucene fuzzy string matching Doesn’t really work Yoox.com 3,000 products called “dress” 7,000 products called “shirt”
How We Detect Duplicates Now BRISK image descriptors Leutenegger, Chli and Siegwart BRISK: Binary Robust Invariant Scalable Keypoints. ICCV 2011: 2548-2555
BRISK octaves How We Detect Duplicates Now
From Descriptors to Image Similarity k-means bag of words 1 x k
Architecture Started in Storm/Java Very painful Ended up in Celery Much nicer Matching is done in elastic search.
Results
Results
Results
Results
Speed We apply some filters but still lots of data Could be doing 1,000,000+ comparisons per image Speed it up with local sensitivity hashing
Local Sensitivity Hashing
Local Sensitivity Hashing
Local Sensitivity Hashing 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 0 0
Local Sensitivity Hashing 10 10 10 10 11 10 10 11 11 11 11 01 00 01 00 01 00 00 00 10 10 00
Local Sensitivity Hashing 101 101 101 101 111 101 100 111 110 110 110 010 000 010 000 010 000 000 000 001 101 101
Local Sensitivity Hashing 1011 1011 1011 1011 1111 1011 1001 1111 1101 1101 1101 0101 0001 0101 0001 0101 0000 0000 0000 0010 1010 1010
Local Sensitivity Hashing 1011 1011 1011 1011 1111 1011 1001 1111 1101 1101 1101 0101 0001 0101 0001 0101 0000 0000 0000 0010 1010 1010
LSH (Random Projections) Split the n-dimensional code in to q r-bit integers. 0 0 0 1 1 1 1 0 1 1 1 0 0 0 1 0 0 0 0 1 = 0 1 1 1 1 0 = 1 4 1 1 1 0 = 1 4 0 0 1 0 = 0 2 Map d-dimensional space into n-dimensional binary code. 0 0 0 1 1 1 1 0 1 1 1 0 0 0 1 0 1 0 0 0 0 0 1 0 1 1 0 1 1 0 0 1 0 0 1 0 1 1 0 0 0 0 1 1 1 1 1 0 1 1 0 0 1 1 0 0 1 1 0 0 1 0 1 0 1 1 1 1 0 0 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 1 1 1 0 1 1 0 0 1 0 1 Use the integer codes (i.e hash) to filter for similarity.
Parameters The Johnson-Lindestrauss lemma states that For a n×m matrix we can project into… d = O(log n) … dimensions and still preserve distance.
Performance …?!
Other “useful” applications
Color Variants
Matching Sets
Outfits
Model Faces
What’s Next Reverse image search This works! We tried during a hackathon Similiar textual features i.e. word embeddings Dual image / text vector embeddings
thank you @ejbell

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