About
Texture Classification is a fundamental issue in computer vision and image processing, playing a significant role in many applications such as medical image analysis, remote sensing, object recognition, document analysis, environment modeling, content-based image retrieval and many more.
Source: Improving Texture Categorization with Biologically Inspired Filtering
Benchmarks
Greatest papers with code
PCANet: A Simple Deep Learning Baseline for Image Classification?
In this work, we propose a very simple deep learning network for image classification which comprises only the very basic data processing components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms.
Ranked #16 on
Image Classification
on MNIST
FACE RECOGNITION FACE VERIFICATION IMAGE CLASSIFICATION OBJECT RECOGNITION TEXTURE CLASSIFICATION
Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data
• felixriese/CNN-SoilTextureClassification
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Thereby, we rely on the freely available LUCAS topsoil dataset.
Wavelet Convolutional Neural Networks for Texture Classification
• shinfj/WaveletCNN_for_TextureClassification
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We propose a novel CNN architecture, wavelet CNNs, which integrates a spectral analysis into CNNs.
Using Filter Banks in Convolutional Neural Networks for Texture Classification
Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself.
Persistence Curves: A canonical framework for summarizing persistence diagrams
For that reason, transforming these diagrams in a way that is compatible with machine learning is an important topic currently researched in TDA.
Wavelet Convolutional Neural Networks
Given that spatial and spectral approaches are known to have different characteristics, it will be interesting to incorporate a spectral approach into CNNs.
IMAGE CLASSIFICATION OBJECT RECOGNITION TEXTURE CLASSIFICATION
Histogram Layers for Texture Analysis
• GatorSense/Histogram_Layer
• 
We present a histogram layer for artificial neural networks (ANNs).
Co-occurrence Based Texture Synthesis
As image generation techniques mature, there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate.
Local Rotation Invariance in 3D CNNs
Locally Rotation Invariant (LRI) image analysis was shown to be fundamental in many applications and in particular in medical imaging where local structures of tissues occur at arbitrary rotations.
Spatio-spectral networks for color-texture analysis
Texture is one of the most-studied visual attribute for image characterization since the 1960s.