Unsupervised Learning for Recognition Pietro Perona California Institute of Technology & Universita...

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Transcript of Unsupervised Learning for Recognition Pietro Perona California Institute of Technology & Universita...

Meet the xyz

Spot the xyz

Object categories

individualobjects

`visual’ categories

`functional’categories*

Part similarity

Importance of `mutual position’

Presence / Absence of Features

occlusion

Background clutter

Unsupervised learning

Unsupervised detector training - 1

• Highly textured neighborhoods are selected automatically• produces 100-1000 patterns per image

10

10

Unsupervised detector training - 2

“Pattern Space” (100+ dimensions)

Unsupervised detector training - 3

100-1000 images ~100 detectors

Parameter Estimation

• Take training images. Consider set of detectors…

• Apply detectors…..

Rear Views of Cars

• 200 Images (100 training, 100 testing)• Only one image per car • High-pass filtered

Preselected Parts

Model Foreground pdf

Sample Detection

Parts in Model

Learned ModelTest Error: 13% (5 Parts)

Detections of Cars

Background Images