ECCV2010: feature learning for image classification, part 0
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Transcript of ECCV2010: feature learning for image classification, part 0
Andrew Ng
Feature learning for image
classificationKai Yu and Andrew Ng
Andrew Ng
Computer vision is hard
Andrew Ng
Machine learning and feature representations
Input
Input spaceMotorbikes“Non”-Motorbikes
Learningalgorithm
pixel 1
pixel 1
pixel 2
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Machine learning and feature representations
Input
Input space Feature spaceMotorbikes“Non”-Motorbikes
Feature representation
Learningalgorithm
pixel 1 “wheel”
handle
wheel
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How is computer perception done?
Image Low-levelvision features
Recognition
Low-level statefeatures Action
Helicopter
Audio Low-levelaudio features
Speakeridentification
Object detection
Audio classification
Helicopter control
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Learning representations
Sensor Learningalgorithm
Feature Representation
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Computer vision features
SIFT Spin image
HoG RIFT
Textons GLOH
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Audio features
ZCR
Spectrogram MFCC
RolloffFlux
Problems of hand-tuned features1. Needs expert knowledge
2. Time-consuming and expensive3. Does not generalize to other domains
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Computer vision is more than pictures
Camera array
3d range scans (flash lidar) Audio
Can we automatically learn good feature representations?
Images
Thermal Infrared
Video
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Learning representations
Sensor Learningalgorithm
Feature Representation
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Sensor representation in the brain
[BrainPort; Martinez et al; Roe et al.]
Seeing with your tongueHuman echolocation (sonar)
Auditory cortex learns to see.
Auditory Cortex
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Unsupervised feature learning
Find a better way to represent images than pixels.
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The goal of Unsupervised Feature Learning
Unlabeled images
Learningalgorithm
Feature representation
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Tutorial outline
1. Current methods.
2. Sparse coding for feature learning.
— Break —
3. Advanced classification.
4. Advanced concepts & deep learning.