# Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation

arXiv: https://arxiv.org/abs/2410.21562 · HTML: https://mlsanity.com/paper/2410.21562/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 85 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Context Encoding for Semantic Segmentation](https://mlsanity.com/paper/1803.08904/) (2018)
- [Deep Visual Attention Prediction](https://mlsanity.com/paper/1705.02544/) (2017)
- [Video Salient Object Detection via Fully Convolutional Networks](https://mlsanity.com/paper/1702.00871/) (2017)
- [From BoW to CNN: Two Decades of Texture Representation for Texture Classification](https://mlsanity.com/paper/1801.10324/) (2018)
- [Deep Texture Manifold for Ground Terrain Recognition](https://mlsanity.com/paper/1803.10896/) (2018)
- [Using Filter Banks in Convolutional Neural Networks for Texture Classification](https://mlsanity.com/paper/1601.02919/) (2016)
- [Fusion of complex networks and randomized neural networks for texture analysis](https://mlsanity.com/paper/1806.09170/) (2018)
- [A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation](https://mlsanity.com/paper/1401.2686/) (2014)
- [Model-based learning of local image features for unsupervised texture segmentation](https://mlsanity.com/paper/1708.00180/) (2017)
- [One-shot Texture Segmentation](https://mlsanity.com/paper/1807.02654/) (2018)
- [Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification](https://mlsanity.com/paper/1703.05530/) (2017)
- [Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrieval](https://mlsanity.com/paper/1709.02463/) (2017)
- [Combining local regularity estimation and total variation optimization for scale-free texture segmentation](https://mlsanity.com/paper/1504.05776/) (2015)
- [Texture Segmentation Based Video Compression Using Convolutional Neural Networks](https://mlsanity.com/paper/1802.02992/) (2018)
- [Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets](https://mlsanity.com/paper/2410.19191/) (2024)

## Abstract (arXiv)

In this paper, we propose a new approach to perform supervised texture classification/segmentation. The proposed idea is to feed a Fully Convolutional Network with specific texture descriptors. These texture features are extracted from images by using an empirical curvelet transform. We propose a method to build a unique empirical curvelet filter bank adapted to a given dictionary of textures. We then show that the output of these filters can be used to build efficient texture descriptors utilized to finally feed deep learning networks. Our approach is finally evaluated on several datasets and compare the results to various state-of-the-art algorithms and show that the proposed method dramatically outperform all existing ones.