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Feature learning for image classification

Feature learning for image classification. Kai Yu and Andrew Ng. Computer vision is hard. Machine learning and feature representations. pixel 1. Learning algorithm. pixel 2. Input. Motorbikes. “Non”-Motorbikes. Input space. pixel 2. pixel 1.

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Feature learning for image classification

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  1. Feature learning for image classification Kai Yu and Andrew Ng

  2. Computer vision is hard

  3. Machine learning and feature representations pixel 1 Learning algorithm pixel 2 Input Motorbikes “Non”-Motorbikes Input space pixel 2 pixel 1

  4. Machine learning and feature representations handle Feature representation Learning algorithm wheel Input Motorbikes “Non”-Motorbikes Input space Feature space “handle” pixel 2 pixel 1 “wheel”

  5. How is computer perception done? Object detection Low-level vision features Image Recognition Audio classification Action Speaker identification Low-level audio features Audio Helicopter Helicopter control Image Grasp point Low-level features Low-level state features

  6. Learning representations Learning algorithm Feature Representation Sensor

  7. Computer vision features SIFT Spin image HoG RIFT GLOH Textons

  8. Audio features MFCC Spectrogram Problems of hand-tuned features 1. Needs expert knowledge 2. Time-consuming and expensive 3. Does not generalize to other domains Flux Rolloff ZCR

  9. Computer vision is more than pictures Visible light image 3d range scan (laser scanner) Images Video Audio 3d range scans (flash lidar) Thermal Infrared Thermal Infrared Camera array Can we automatically learn good feature representations? Key question: Can we automatically learn a good feature representation?

  10. Learning representations Learning algorithm Feature Representation Sensor

  11. Sensor representation in the brain Seeing with your tongue Human echolocation (sonar) Auditory cortex learns to see. Auditory Cortex [BrainPort; Martinez et al; Roe et al.]

  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.

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