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ECCV2010: feature learning for image classification, part 0
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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.)

Transcript

  • 1. Feature learning for image classification Kai Yu and Andrew Ng
  • 2. Computer vision is hard
  • 3. Machine learning and feature representations Input Input space Learning algorithm pixel 1 Motorbikes “ Non”-Motorbikes pixel 1 pixel 2
  • 4. Machine learning and feature representations Input Input space Feature space Feature representation Learning algorithm pixel 1 “ wheel” Motorbikes “ Non”-Motorbikes handle wheel
  • 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. Learning representations Sensor Learning algorithm Feature Representation
  • 7. Computer vision features SIFT Spin image HoG RIFT Textons GLOH
  • 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. 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. Learning representations Sensor Learning algorithm Feature Representation
  • 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. Unsupervised feature learning
    • Find a better way to represent images than pixels.
  • 13. The goal of Unsupervised Feature Learning Unlabeled images Learning algorithm Feature representation
  • 14. Tutorial outline
    • Current methods.
    • Sparse coding for feature learning.
      • — Break —
    • Advanced classification.
    • Advanced concepts & deep learning.