Deep structured features for semantic segmentation


Michael Tschannen, Lukas Cavigelli, Fabian Mentzer, Thomas Wiatowski, and Luca Benini


Proc. of European Signal Processing Conference (EUSIPCO), pp. 61-65, Sept. 2017.

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We propose a highly structured neural network architecture for semantic segmentation with an extremely small model size, suitable for low-power embedded and mobile platforms. Specifically, our architecture combines i) a Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random layer realizing a radial basis function kernel approximation, and iii) a linear classifier. While stages i) and ii) are completely pre-specified, only the linear classifier is learned from data. We apply the proposed architecture to outdoor scene and aerial image semantic segmentation and show that the accuracy of our architecture is competitive with conventional pixel classification CNNs. Furthermore, we demonstrate that the proposed architecture is data efficient in the sense of matching the accuracy of pixel classification CNNs when trained on a much smaller data set.


semantic segmentation, deep feature extraction, Haar wavelets, small memory footprint

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Copyright Notice: © 2017 M. Tschannen, L. Cavigelli, F. Mentzer, T. Wiatowski, and L. Benini.

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