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