Titlebook: Probabilistic and Biologically Inspired Feature Representations; Michael Felsberg Book 2018 Springer Nature Switzerland AG 2018

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Conclusions,been to conclude the work on channel representations and to produce a comprehensive review of all details, but to illustrate the use of the channel representation as a mathematical and algorithmic tool.

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Probabilistic and Biologically Inspired Feature Representations

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Introduction,a suitable way, where the definition of . has been shifting regularly, resulting in various feature design principles. Also, after the recent progress of deep learning, and deep features, these principles are still relevant for understanding and improving deep learning functionality and methodology.

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Channel Coding of Features, for channel coding, first in one (feature) dimension, then in several dimensions. These definitions will be used to define CCFMs and relate them to popular specific feature representations such as SIFT, HOG, and SHOT in Chapter 4.

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Channel-Coded Feature Maps, for channel coding of features to spatial dimensions and thus introduce CCFMs. Using its formal definition, popular specific feature representations such as SIFT and HOG (2D) and SHOT (3D) will be expressed in terms of CCFMs.

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