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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Hand-Designed Local Image Descriptors vs. Off-the-Shelf CNN-Based Features for Texture Classification: An Experimental Comparison

verfasst von : Raquel Bello-Cerezo, Francesco Bianconi, Silvia Cascianelli, Mario Luca Fravolini, Francesco di Maria, Fabrizio Smeraldi

Erschienen in: Intelligent Interactive Multimedia Systems and Services 2017

Verlag: Springer International Publishing

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Abstract

Convolutional Neural Networks have proved extremely successful in object classification applications; however, their suitability for texture analysis largely remains to be established. We investigate the use of pre-trained CNNs as texture descriptors by tapping the output of the last fully connected layer, an approach that has proved its effectiveness in other domains. Comparison with classical descriptors based on signal processing or statistics over a range of standard databases suggests that CNNs may be more effective where the intra-class variability is large. Conversely, classical approaches may be preferable where classes are well defined and homogeneous.

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Nächstes Kapitel Images Selection and Best Descriptor Combination for Multi-shot Person Re-identification
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Source: Scopus®; visited on Januray 18, 2017.
 
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Metadaten
Titel
Hand-Designed Local Image Descriptors vs. Off-the-Shelf CNN-Based Features for Texture Classification: An Experimental Comparison
verfasst von
Raquel Bello-Cerezo
Francesco Bianconi
Silvia Cascianelli
Mario Luca Fravolini
Francesco di Maria
Fabrizio Smeraldi
Copyright-Jahr
2018
Buch
Intelligent Interactive Multimedia Systems and Services 2017

Print ISBN: 978-3-319-59479-8

Electronic ISBN: 978-3-319-59480-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-59480-4_1

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