A Hybrid Deep Learning Approach for Texture Analysis
This work addresses texture classification for applications like remote sensing and forest species recognition, but it is incremental as it builds on existing methods.
The paper tackled the problem of texture classification's lack of generalization across datasets by proposing a hybrid deep learning approach combining CNN and SVM, resulting in stable classification rates across different datasets.
Texture classification is a problem that has various applications such as remote sensing and forest species recognition. Solutions tend to be custom fit to the dataset used but fails to generalize. The Convolutional Neural Network (CNN) in combination with Support Vector Machine (SVM) form a robust selection between powerful invariant feature extractor and accurate classifier. The fusion of experts provides stability in classification rates among different datasets.