Publication: A tutorial review on entropy-based handcrafted feature extraction for information fusion
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Undergraduate course
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Acesso aberto
Abstract
Entropy (H) is the main subject of this article, concisely written to serve as a tutorial introducing two feature extraction (FE) methods for usage in digital signal processing (DSP) and pattern recognition (PR). The theory, carefully exposed, is supplemented with numerical cases, augmented with C/C++ source-codes and enriched with example applications on restricted-vocabulary speech recognition and image synthesis. Complementarily and as innovatively shown, the ordinary calculation of H corresponds to the outcome of a partially pre-tuned deep neural network architecture which fuses important information, bringing a cutting-edge point-of-view for both DSP and PR communities.
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Keywords
Deep networks, Entropy, Handcrafted feature extraction, Image synthesis, Information fusion, Restricted-vocabulary speech recognition
Language
English
Citation
Information Fusion, v. 41, p. 161-175.
