Fashion product de-duplication with image similarity and LSH

•

9 gostaram•5,201 visualizações

Eddie Bell

My talk from pydata London 12/2014

Dados e análise

Matching Fashion Products
With LSH Image Similarity

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.

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
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

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.

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

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This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

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Paris Women in Machine Learning and Data Science

Fashion product de-duplication with image similarity and LSH

1. Matching Fashion Products With LSH Image Similarity

2. Hi, I’m Eddie. @ejbell

3. We collect the world of fashion into a customisable shopping experience. 3

4. 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

5. Why do we get duplicates? There is no ISBN for fashion Burberry Selfridges inter-retailer intra-retailer intra-retailer

6. 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”

7. How We Detect Duplicates Now BRISK image descriptors Leutenegger, Chli and Siegwart BRISK: Binary Robust Invariant Scalable Keypoints. ICCV 2011: 2548-2555

8. BRISK octaves How We Detect Duplicates Now

9. From Descriptors to Image Similarity k-means bag of words 1 x k

10. Architecture Started in Storm/Java Very painful Ended up in Celery Much nicer Matching is done in elastic search.

11. Results

12. Results

13. Results

14. Results

15. 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

16. Local Sensitivity Hashing

17. Local Sensitivity Hashing

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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.

24. 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.

25. Performance …?!

26. Other “useful” applications

27. Color Variants

28. Matching Sets

29. Outfits

30. Model Faces

31. 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

32. thank you @ejbell

Fashion product de-duplication with image similarity and LSH

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