Wave Loss: A Topographic Metric for Image Segmentation

Ákos Kovács, Jalal Al-Afandi, Csaba Botos and András Horváth
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Ákos Kovács: Faculty of Information Technology and Bionics, Peter Pazmany Catholic University, 1083 Budapest, Hungary
Jalal Al-Afandi: Faculty of Information Technology and Bionics, Peter Pazmany Catholic University, 1083 Budapest, Hungary
Csaba Botos: Department of Engineering Science, University of Oxford, Oxford OX1 2JD, UK
András Horváth: Faculty of Information Technology and Bionics, Peter Pazmany Catholic University, 1083 Budapest, Hungary

Mathematics, 2022, vol. 10, issue 11, 1-15

Abstract: The solution of segmentation problems with deep neural networks requires a well-defined loss function for comparison and network training. In most network training approaches, only area-based differences that are of differing pixel matter are considered; the distribution is not. Our brain can compare complex objects with ease and considers both pixel level and topological differences simultaneously and comparison between objects requires a properly defined metric that determines similarity between them considering changes both in shape and values. In past years, topographic aspects were incorporated in loss functions where either boundary pixels or the ratio of the areas were employed in difference calculation. In this paper we will show how the application of a topographic metric, called wave loss, can be applied in neural network training and increase the accuracy of traditional segmentation algorithms. Our method has increased segmentation accuracy by 3% on both the Cityscapes and Ms-Coco datasets, using various network architectures.

Keywords: instance segmentation; topographic loss; neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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