Rotation invariant CNN using scattering transform for image classification

• URL: http://arxiv.org/abs/2105.10175v1
• Date: Fri, 21 May 2021 07:36:34 GMT
• Title: Rotation invariant CNN using scattering transform for image classification
• Authors: Rosemberg Rodriguez Salas (LIGM), Eva Dokladalova (LIGM), Petr Dokl\'adal (CMM)
• Abstract summary: We propose a convolutional predictor that is invariant to rotations in the input. The architecture is capable of predicting the angular orientation without angle-annotated data. We validate the results by training with upright and randomly rotated samples.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep convolutional neural networks accuracy is heavily impacted by rotations of the input data. In this paper, we propose a convolutional predictor that is invariant to rotations in the input. This architecture is capable of predicting the angular orientation without angle-annotated data. Furthermore, the predictor maps continuously the random rotation of the input to a circular space of the prediction. For this purpose, we use the roto-translation properties existing in the Scattering Transform Networks with a series of 3D Convolutions. We validate the results by training with upright and randomly rotated samples. This allows further applications of this work on fields like automatic re-orientation of randomly oriented datasets.

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