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Scale equivariant CNNs with scale steerable filters

Naderi, H ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/MVIP49855.2020.9116889
  3. Publisher: IEEE Computer Society , 2020
  4. Abstract:
  5. 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 FMNIST-scale dataset. © 2020 IEEE
  6. Keywords:
  7. Convolutional neural networks ; Equivariance ; Image classification ; Invariance ; Scale ; Steerable Filters ; Classification (of information) ; Computer vision ; Cost functions ; Classification methods ; Convolution neural network ; Geometric transformations ; Isotropic scaling ; Model parameters ; Scale equivariance ; Structural constraints ; Image enhancement
  8. Source: Iranian Conference on Machine Vision and Image Processing, MVIP, 19 February 2020 through 20 February 2020 ; Volume 2020-February , 2020 ; ISSN: 21666776 ; ISBN: 9781728168326
  9. URL: https://ieeexplore.ieee.org/document/9116889