MIT CSAIL Vision interfaces Towards efficient matching with random hashing methods… Kristen...
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MIT CSAILVision interfaces
Towards efficient matching with random hashing methods…
Kristen GraumanGregory Shakhnarovich
Trevor Darrell
MIT CSAILVision interfaces
Motivation: Content-based image retrieval
Data set of 30 scenes in Boston• 1,079 database images• 89 query images
Features:• Harris-Affine detector (max m=3,595)
• MSER detector(max m=1,707)
• SIFT-PCA descriptors
Query
MIT CSAILVision interfaces
Content-based image retrieval
Pyramid match: ~1 second / query
Optimal match: ~2 hours / query
Number top retrievals
Acc
ura
cy
Even this is far too slow forany web-scale application!
MIT CSAILVision interfaces
Sub-linear time image search
N
<< N
h0111101
0110111
0110101
Randomized hashing techniques useful for sub-linear query time of very large image databasesN
Linear scan
MIT CSAILVision interfaces
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 [Charikar 2002]. [Grauman PhD 2006]
MIT CSAILVision interfaces
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].)
MIT CSAILVision interfaces
Approximate nearest neighbor techniques
… … …Rendered (& hashed)PoseDB
input
Hashfcns.
similar examples fall into same bucket in one or more hash table
MIT CSAILVision interfaces
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]
MIT CSAILVision interfaces
Pose estimation and Similarity-sensitive hashing
… … …Rendered (& hashed)PoseDB
input
Pose-sensitiveHashfcns.
NN similar in pose, not image
[Shakhnarovich et. al ’03, Shakhnarovich ‘05]
MIT CSAILVision interfaces
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]
MIT CSAILVision interfaces
PSH results
~200,000 examples in DB; 2 sec
[Shakhnarovich et al. 2003, 2005]
MIT CSAILVision interfaces
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.