Deep roto-translation scattering for object classification

Dictionary learning algorithms or supervised deep convolution networks have considerably improved the efficiency of predefined feature representations such as SIFT. We introduce a deep scattering convolution network, with complex wavelet filters over spatial and angular variables. This representatio...

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Bibliographic Details
Published in2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 2865 - 2873
Main Authors Oyallon, Edouard, Mallat, Stephane
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.06.2015
Subjects
Computer architecture
Computer vision
Convolution
Dictionaries
Learning
Networks
Pattern recognition
Representations
Scattering
Support vector machines
Three-dimensional displays
Wavelet transforms
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ISSN1063-6919
1063-6919
2575-7075
DOI10.1109/CVPR.2015.7298904

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Summary:Dictionary learning algorithms or supervised deep convolution networks have considerably improved the efficiency of predefined feature representations such as SIFT. We introduce a deep scattering convolution network, with complex wavelet filters over spatial and angular variables. This representation brings an important improvement to results previously obtained with predefined features over object image databases such as Caltech and CIFAR. The resulting accuracy is comparable to results obtained with unsupervised deep learning and dictionary based representations. This shows that refining image representations by using geometric priors is a promising direction to improve image classification and its understanding.
Bibliography:ObjectType-Article-2
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SourceType-Conference Papers & Proceedings-2
ISSN:1063-6919
1063-6919
2575-7075
DOI:10.1109/CVPR.2015.7298904