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Towards efficient matching with random hashing methods… Kristen Grauman Gregory Shakhnarovich Trevor Darrell PowerPoint Presentation
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Towards efficient matching with random hashing methods… Kristen Grauman Gregory Shakhnarovich Trevor Darrell . Query. Motivation: Content-based image retrieval. Features: Harris-Affine detector (max m =3,595) MSER detector (max m =1,707) SIFT-PCA descriptors.

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slide1

Towards efficient matching with random hashing methods…Kristen GraumanGregory Shakhnarovich Trevor Darrell

motivation content based image retrieval

Query

Motivation: Content-based image retrieval
  • Features:
  • Harris-Affine detector
  • (max m=3,595)
  • MSER detector
    • (max m=1,707)
  • SIFT-PCA descriptors
  • Data set of 30 scenes in Boston
  • 1,079 database images
  • 89 query images
content based image retrieval

Pyramid match: ~1 second / query

Optimal match: ~2 hours / query

Content-based image retrieval

Even this is far too slow for

any web-scale application!

Accuracy

Number top retrievals

sub linear time image search

?

N

0110101

?

h

0110111

0111101

<< N

Linear scan

Sub-linear time image search

Randomized hashing techniques useful for sub-linear query time of very large image databases

N

pyramid match hashing
Pyramid match hashing
  • For fixed-size sets, Locality-Sensitive Hashing [Indyk & Motwani 1998] provides bounded approximate similarity search over bijective matching [Indyk & Thaper 2003]; [Grauman & Darrell CVPR 2004, 2005]
  • For varying set sizes, embedding of pyramid match (with product normalization) makes random hyperplane hashing possible under set intersection hash family of [Charikar2002]. [Grauman PhD 2006]
single frame pose estimation via approximate nearest neighbor regression
Single Frame Pose Estimation via Approximate Nearest Neighbor regression
  • Obtain large DB of pose-appearance mappings
  • Exploit fast methods for approximate nearest neighbor search in high dim. spaces. (e.g., LSH [Indyk and Motwani ‘98-’00].)
approximate nearest neighbor techniques

… … …

Rendered (& hashed)

Pose

DB

Approximate nearest neighbor techniques

Hash

fcns.

input

similar examples fall into same bucket

in one or more hash table

single frame pose estimation via approximate nearest neighbor regression1
Single Frame Pose Estimation via Approximate Nearest Neighbor regression
  • Render large DB of pose-appearance mappings
  • Exploit fast methods for approximate nearest neighbor search in high dim. spaces. (e.g., LSH [Indyk and Motwani ‘98-’00].)

Problem: signal distance dominated by nuisance variables

Idea: find embedding (i.e., hash functions for LSH) most relevant to parameter (pose) similarity… [Shakhnarovich et. al ’03, Shakhnarovich ‘05]

pose estimation and similarity sensitive hashing
Pose estimation and Similarity-sensitive hashing

… … …

Rendered (& hashed)

Pose

DB

Pose-

sensitive

Hash

fcns.

input

NN similar in pose, not image

[Shakhnarovich et. al ’03, Shakhnarovich ‘05]

sse boostpro
SSE / BoostPro

Similarity Sensitive Embedding

  • Compute embedding H: I  {0, 1}N such that

| H(I(1)) - H(I(2)) | is small if 1 is close to 2

| H(I(1)) - H(I(2)) | is large otherwise

  • Use the embedding with approximate nearest neighbors retrieval (LSH)
  • Find H by training boosted classifier to learn “same-pair” and concatenate resulting weak learners …

[Shakhnarovich 2005]

psh results
PSH results

~200,000 examples in DB; 2 sec

[Shakhnarovich et al. 2003, 2005]

conclusions
Conclusions
  • Random Hashing techniques allow broad search; well suited for very high dimensional spaces
  • Useful in domains where there is no prior knowledge about how to cluster or model data…
  • Similarity (parameter) sensitive hashing can find distance related to task…effectively learn problem dependent distance measure and efficient means to index.