Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation

TL;DR

This paper introduces a supervised texture segmentation method combining empirical curvelet transform-based features with a Fully Convolutional Network, significantly outperforming existing algorithms on multiple datasets.

Contribution

It presents a novel approach that integrates empirical curvelet texture descriptors with deep learning for improved texture segmentation accuracy.

Findings

Outperforms state-of-the-art algorithms on multiple datasets

Effective texture descriptors built from empirical curvelet filters

Significant improvement in segmentation accuracy

Abstract

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.