Contour Fragments

Contour-Based Learning:

A two-stage, partially supervised learning architecture is proposed:

  1. a rudimentary detector is learned from a very small set of segmented images and applied to a larger training set of unsegmented images;
  2. the second stage bootstraps these detections to learn an improved classifier while explicitly training against clutter

QQ截图20121207152240

 

scale matter: 

  • Image patterns of different statistical properties are connected by scale
  • change of viewing distance
  • change of camera resolution
  • Change of statistical/information

 

1

  1. Smoothing reduces entropy rate (by small fixed amount)
  2. Subsampling increases entropy rate (can be big)
•Different image patterns reside in different entropy regimes
•Different entropy regimes are connected by scaling
•Different statistical models within a common framework

2

 

Mid-Entropy regime

QQ截图20121207181842

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