# Shearlets as Feature Extractor for Semantic Edge Detection: The Model-Based and Data-Driven Realm

arXiv: https://arxiv.org/abs/1911.12159 · HTML: https://mlsanity.com/paper/1911.12159/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Explaining Image Classifiers with Multiscale Directional Image Representation](https://mlsanity.com/paper/2211.12857/) (2022)
- [Extrapolation Frameworks in Cognitive Psychology Suitable for Study of Image Classification Models](https://mlsanity.com/paper/2112.03411/) (2021)
- [CoShNet: A Hybrid Complex Valued Neural Network using Shearlets](https://mlsanity.com/paper/2208.06882/) (2022)
- [Federated Learning without Revealing the Decision Boundaries](https://mlsanity.com/paper/2103.00695/) (2021)
- [Community Detection in Medical Image Datasets: Using Wavelets and Spectral Methods](https://mlsanity.com/paper/2112.12021/) (2021)

## Abstract (arXiv)

Semantic edge detection has recently gained a lot of attention as an image processing task, mainly due to its wide range of real-world applications. This is based on the fact that edges in images contain most of the semantic information. Semantic edge detection involves two tasks, namely pure edge detecion and edge classification. Those are in fact fundamentally distinct in terms of the level of abstraction that each task requires, which is known as the distracted supervision paradox that limits the possible performance of a supervised model in semantic edge detection. In this work, we will present a novel hybrid method to avoid the distracted supervision paradox and achieve high-performance in semantic edge detection. Our approach is based on a combination of the model-based concept of shearlets, which provides probably optimally sparse approximations of a model-class of images, and the data-driven method of a suitably designed convolutional neural netwok. Finally, we present several applications such as tomographic reconstruction and show that our approach signifiantly outperforms former methods, thereby indicating the value of such hybrid methods for the area in biomedical imaging.