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This final chapter provides an introduction into classification and learning, with a detailed description of basic AdaBoost and the use of random forests. These concepts are illustrated by applications for face detection, and for pedestrian detection, respectively.
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In classification theory, a descriptor is usually also called a feature. A feature in an image that, as commonly used in image analysis, combines a keypoint and a descriptor. Thus, we continue to use “descriptor” rather than “feature” for avoiding confusion.
This is the error defined in margin-based classifiers such as support vector machines. This error is (usually) not explicitly used in AdaBoost.
Shannon’s entropy corresponds to minus the entropy used in thermodynamics.
The tree diagram has its root at the top (as is customary). For people who complain that it is misleading to depict a tree with its root at the top, here are two examples: Inside the large Rikoriko Cave in The Poor Knights Islands (New Zealand) some trees are growing down from the roof, and on coastal cliffs around northern New Zealand many large Pohutukawa trees are rooted at the edge of a cliff, with almost all of the tree sprawlings down the cliff, lower than its root.
The TUD Multiview Pedestrians database is available at www.d2.mpi-inf.mpg.de/node/428 for free download.
- Object Detection
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