2013
CVPR
CVPR 2013
Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination
Abstract
An affine invariant representation is constructed with a cascade of invariants, which preserves information for classification. A joint translation and rotation invariant representation of image patches is calculated with a scattering transform. It is implemented with a deep convolution network, which computes successive wavelet transforms and modulus non-linearities. Invariants to scaling, shearing and small deformations are calculated with linear operators in the scattering domain. State-of-the-art classification results are obtained over texture databases with uncontrolled viewing conditions.
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Conference Pioneer
— CVPR 2013
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Interdisciplinary Bridge
— Computer Vision and Deep Learning and Machine Learning and Mathematics & Optimization
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Trend Setter
— Convolutional Neural Networks
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Keyword Pioneer
— rotation invariance
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Hot Topic Early Bird
— wavelet transform
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Deep Learning > Architectures > Neural Networks
Computer Vision > Analysis > 3D Vision
Mathematics & Optimization > Optimization > Continuous Optimization
Computer Vision > Analysis > Image Classification
Deep Learning > Architectures > Convolutional Neural Networks