Texture Classification via Attention Based Random Encoded Activation Maps

Texture classification is a challenging and pivotal task in the field of computer vision. Recently, methods that extract features through a frozen backbone have shown great potential due to the growing computing cost of large models. However, these methods overlook the interactional information among activation maps at varying depths. To address this issue, we propose Attention Based Random Encoded Activation Maps(AREAM), which utilizes the Convolutional Block Attention Module(CBAM) to extract richer texture information from activation maps at different depths. After processing the activation maps through random auto-encoders, we employ Kernel Principal Component Analysis(KPCA) to eliminate less significant information and reduce dimension. Subsequently, the data is fed into a non-linear Support Vector Machine(SVM) for classification. The experimental results across multiple texture datasets demonstrate the effectiveness of our method.