Feature learning  for image classification Kai Yu and Andrew Ng
Computer vision is hard
Machine learning and feature representations Input Input space Learning algorithm pixel 1 Motorbikes “ Non”-Motorbikes pix...
Machine learning and feature representations Input Input space Feature space Feature representation Learning algorithm pix...
How is computer perception done? Object detection Audio classification Helicopter control  Image Low-level vision features...
Learning representations Sensor Learning algorithm Feature Representation
Computer vision features SIFT Spin image HoG RIFT Textons GLOH
Audio features ZCR Spectrogram MFCC Rolloff Flux Problems of hand-tuned features 1. Needs expert knowledge 2. Time-consumi...
Computer vision is more than pictures Can we automatically learn good feature representations? Key question: Can we automa...
Learning representations Sensor Learning algorithm Feature Representation
Sensor representation in the brain [BrainPort; Martinez et al; Roe et al.] Human echolocation (sonar) Auditory cortex lear...
Unsupervised feature learning <ul><li>Find a better way to represent images than pixels. </li></ul>
The goal of Unsupervised Feature Learning Unlabeled images Learning algorithm Feature representation
Tutorial outline <ul><li>Current methods.  </li></ul><ul><li>Sparse coding for feature learning.  </li></ul><ul><ul><li>— ...
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ECCV2010: feature learning for image classification, part 0

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  • Good faith effort to implement state-of-the-art. Cluttered scenes.
  • Where do we get these low-level representations from?
  • XXX AI dedicated substantial amount of effort to high-level feature representations. XXX Should now dedicate equal amount to low-level feature representations, because they’re what’s really needed to get our systems to work.
  • Where do we get these low-level representations from?
  • Goal of workshop: Give you high level overview of some of the ideas in ufl. Also, give you ability to go home and start implementing something. (At end, will have resources.)
  • ECCV2010: feature learning for image classification, part 0

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

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