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WISE: Large Scale Content-Based Web Image Search Michael Isard Joint with: Qifa Ke, Jian Sun, Zhong Wu Microsoft Research Silicon Valley 1
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WISE: Large Scale Content-Based Web Image Search
Michael IsardJoint with: Qifa Ke, Jian Sun, Zhong Wu
Microsoft Research Silicon Valley
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What leaf ?
Artist ? Higher resolution?
Bank web-site?
Email ?
Ad. in e-bay ?
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©: Who else using this?
Query by Images“A picture is worth a thousand words.”
Partial-Duplicate Image Search
• Given a query image, find its partial duplicates from a database of web-images
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Two Major Challenges
• How to represent images– No text annotations or labels– Noise and modification
• How to efficiently index and query images– Large number of images (millions)
Index
?
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Image Representation: Bag-of-Words[CVPR’09, ICCV’09]
Descriptor ID
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3506
999,999
1,000,000
Detection[Lowe 2004, Matas et al 2002,Winder et al 2007]
Bag-of-words [Sivic&Zisserman’2003]
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Code-book
Normalization
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Description[Lowe 2004, Winder et al 2007]
3506
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2: Quantization1: Feature extraction: Bundle Features 3: Representation
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…
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Matching query to database
• Use an index– Each visual word has a ‘posting list’– Lists every image containing the word
• At query time– Look up the posting list for each query word– Merge lists to find candidate images
• Partial match: don’t need every word to be present
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How much work to query?
• Disk-based index, bottleneck is random reads– One seek per posting list
• Also one seek per matching image– To fetch thumbnail etc
• Keep as little information as possible in posting lists, to keep index size small
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Index Pipeline• Implemented in a large computer cluster
– 256-nodes, using Dryad/DryadLINQ
Image Crawler
Content Chunk Visual WordsFeature
Quantizer
Crawler
BundledFeatures
Feature Extractor
Media DB(Thumbnail, URL)
Local Features
Indexer
InvertedIndex
IndexVisual Word
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Query Pipeline
BundledFeatures
Feature Extractor Visual WordsFeature
QuantizerQuery Image
InvertedIndex
Index Server
Search Results(chunkID, imgID)
Media DB(Thumbnail, URL)
GUIResults
…
Bag-of-Words: Limitations
• Quantization– Lost discriminative
power– Sensitive to image
variations and noises– Soft quantization
[Philbin et al, CVPR 2008]
– Hamming embedding [Jegou et al, ECCV 2008]
Descriptor ID
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1000,000
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Quantization
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Geometric verification
• In practice, bag of words is too weak• Does not exploit any geometry• Post-process to check spatial layout of
matching features• Requires a disk seek per image
– Only used as a re-ranking step to shortlist of matched images
Geometric Re-ranking
Re-rank top 300 images
50000 200000 500000 10000000.25
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0.35
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0.75
Number of images
mA
P
baseline (bag-of-words)
baseline + reranking
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Geometry in the index
• Previous works:– Jegou et al ECCV 2008
• Try to match similar orientations and scales
– Perdoch et al CVPR 2009• Match oriented features more effectively
• Still feature-by-feature– Global geometric consitency applied at the end
Single Feature is Weak
++
Neighboring Features ?
Define Neighboring Features
• Previous works– kNN voting [Sivic&Zisserman 2003]
– Higher-order spatial features [Liu et al][Yuan et al][Tirilly et al][Quack et al]
– Post geometric spatial verification [Lowe’2004][Chum et al 2007][Nister 2006][Philbin et al 2007]……
– Geometric Min-Hash [Chum et al 2009]
• Challenges– Repeatable– Partial matching– Scalable: simple enough to build into index
Define Neighboring Features
DoG Features [Lowe 2004] MSER Features [Matas et al 2002]
- point features- repeatable
- region features- repeatable
Define Neighboring Features
DoG Features [Lowe 2004] MSER Features [Matas et al 2002]
- point features- repeatable
- region features- repeatable
region groups points?
Bundled Feature: Definition
• Bundled Feature = A set of DOG features bundled by a MSER region
DoG interest points
MSER region
Bundled Feature: DefinitionBundled Features
Matched bundlep = { pi }
Query bundleq = {qj} = { }
Matching Bundles: MembershipMembership score:
Voting weight:
( ; ) 4mM q p q p
1 2( , ) ( ) 4 16
j j
jq q
Sim I I v q
( ) ( ; ) 4j mv q M q p
Matched bundlesp1, p2, p3
Query bundleq = {qj} = { }
Membership score:
Matching Bundles: Membership
p1
p2
p3
1 1( ; ) 2mM p pq q
2 2( ; ) 1mM p pq q
3 3( ; ) 2mM p pq q
1 2( , ) ( ) 8
j
jq
Sim I I v q 1( ) 2v q 2( ) 2v q
3( ) max(1,2) 2v q
4( ) 2v q
( ) max ( ; ) |j m jv q M q k
kp
q p q
Matching Bundles: Geometric Constraint
1
3
2
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1
3
2
5
order in target img: 1 < 3 < 4 < 5
order inconsistency: 0 + 0 + 0 = 0
Penalize inconsistent relative orders:
y
1 < 2 < 3 < 4order in query img: 1 < 2 < 3 < 4
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5 > 2 > 1 < 3
1 + 1 + 0 = 2
matching order:
inconsistency:
order in query img:
query candidate query candidate
1( ; )g q i q iM O p O p q p
( ; ) ( ; ) ( ; )m gM M M q p q p q p
Matching Bundles: Formulation
• Bundle matching score:
• Image matching score:geometric constraintmembership
1 2{ }
( , ) ( )j
jq
Sim I I v q
( ) max ( ; ) |j jv q M q k
kp
q p q
- Repeatable- Partial matching- Scalable?
Inverted Index (without Bundles)Posting ……
Image ID
Visualword
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… … … 27
… 27
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Image ID = 27
Inverted Index with BundlesPosting ……
Image ID
Visualword
……
… … … 27,[3,1,1]
…27,
[1,2,1]…
…
……
Image ID = 27
Bundle Bits
9 bits 5 bits 5 bits
Bundle ID X-Order Y-Orderp1
p2
p3
1
3
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27,[2,2,2
]
RetrievalQuery Image Iq Inverted index with bundle bits
1,1,[2,5,9]
3,1,[3,4,5]
9,2,[3,2,5]
10, 1,[1,1,2]
12,1,[1.1.2]
… 10, 1,[2,2,1] … … …
……
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……
p1
p2
p3
Top candidate images
Experimental Settings
• Image database:– 1M web images from query-click log
• Ground truth partial duplicates– 780 known partial duplicate images in 19 groups
• Baseline bag-of-words– Visual word vocabulary size = 1 M– Soft quantization factor = 4– 500 features per image
Partial Duplicate Example
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Partial Duplicate Example
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Example Query Results
Challenging cases
Query
Evaluation: Precision-Recall
• A query returns N images– T : correct matches– A : expected matches
Precision = T
NRecall =
T
A
Recall
Prec
isio
n
Comparison: Precision-Recall
Baseline bag-of-words (started from 13th)
Bundled features (started from 13th)
Query image:
More Precision-Recall Comparisons
• Average Precision (AP) for one query: – Area under Precision-Recall curve
• mAP: mean of AP’s from all testing queries
Evaluation: mAP
AP
mAP: Baseline Bag-of-Words
50000 200000 500000 1000000
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Number of images
mA
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baselineHEbundled(membership)bundledbundled + HE
mAP: Hamming Embedding (HE)
50000 200000 500000 1000000
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Number of images
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baselineHEbundled(membership)bundledbundled + HE
mAP: Bundle (Membership)
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Number of images
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baselineHEbundled(membership)bundledbundled + HE
mAP: Bundle (both terms)
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Number of images
mA
P
baselineHEbundled(membership)bundledbundled + HE
26% 40%
mAP: Bundle + HE
50000 200000 500000 1000000
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Number of images
mA
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baselineHEbundled(membership)bundledbundled + HE
49%
Bundle VS. Geometric Re-ranking
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Number of images
mA
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baseline (bag-of-words)bundlebaseline + rerankingbundle + reranking
Re-rank top 300 images
Bundle + Geometric Re-ranking
50000 200000 500000 10000000.25
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Number of images
mA
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baseline (bag-of-words)bundlebaseline + rerankingbundle + reranking
Re-rank top 300 images
24% 77%
More Results
Failure Case
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Demo Setup
Document Server
Web Server
Index Servers
Client
6 million images
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Demo
Query image
Results
Conclusion
• Bundle feature– More discriminative– Enforce spatial constraints while traversing index– Partial match– Scalable: built into index
9 bits 5 bits 5 bits
Bundle ID X-Order Y-Order
Thanks!
