Visualizing key features in X-ray images of epoxy resins for improved material classification using singular value decomposition of deep learning features
This work addresses a domain-specific problem for materials science by improving classification of epoxy resins from X-ray images, though it is incremental as it builds on existing deep learning and decomposition methods.
The paper tackled the challenge of visually identifying characteristic features in X-ray images of epoxy resins for material classification by using singular value decomposition on deep learning feature maps, achieving high classification accuracy and robust feature representation.
Although the process variables of epoxy resins alter their mechanical properties, the visual identification of the characteristic features of X-ray images of samples of these materials is challenging. To facilitate the identification, we approximate the magnitude of the gradient of the intensity field of the X-ray images of different kinds of epoxy resins and then we use deep learning to discover the most representative features of the transformed images. In this solution of the inverse problem to finding characteristic features to discriminate samples of heterogeneous materials, we use the eigenvectors obtained from the singular value decomposition of all the channels of the feature maps of the early layers in a convolutional neural network. While the strongest activated channel gives a visual representation of the characteristic features, often these are not robust enough in some practical settings. On the other hand, the left singular vectors of the matrix decomposition of the feature maps, barely change when variables such as the capacity of the network or network architecture change. High classification accuracy and robustness of characteristic features are presented in this work.