Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation
arXiv:2410.21562 · doi:10.1016/j.neucom.2019.04.021
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.
References in corpus (9)
- Empirical wavelet transform
- Video Salient Object Detection via Fully Convolutional Networks
- Deep Visual Attention Prediction
- 2D Empirical Transforms. Wavelets, Ridgelets and Curvelets revisited
- Context Encoding for Semantic Segmentation
- Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrieval
- Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets
- Model-based learning of local image features for unsupervised texture segmentation
- Deep Texture Manifold for Ground Terrain Recognition