The 11th Iranian and the first International Conference on Machine Vision and Image Processing

Scale Equivariant CNNs with Scale Steerable Filters

Hanieh Naderi, Leili Goli, Shohreh Kasaei
The 11th Iranian and the first International Conference on Machine Vision and Image Processing (MVIP 2020)

Abstract

Convolution Neural Networks (CNNs), despite being one of the most successful image classification methods, are not robust to most geometric transformations (rotation, isotropic scaling) because of their structural constraints. Recently, scale steerable filters have been proposed to allow scale invariance in CNNs. Although these filters enhance the network performance in scaled image classification tasks, they cannot maintain the scale information across the network. In this paper, this problem is addressed. First, a CNN is built with the usage of scale steerable filters. Then, a scale equivariat network is acquired by adding a feature map to each layer so that the scale-related features are retained across the network. At last, by defining the cost function as the cross entropy, this solution is evaluated and the model parameters are updated. The results show that it improves the perfromance about 2% over other comparable methods of scale equivariance and scale invariance, when run on the FMNISTscale dataset.

Keywords: Image Classification, Equivariance, Invariance, Convolutional Neural Networks, Scale, Steerable Filters



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