Analysis of activation maps through global pooling measurements for texture classification

We analyzed the effects of global pooling measurements on extracting relevant texture information from a given set of activation maps. Initially, using a layer-by-layer approach (GP-CNN), we experimentally demonstrated that layers at various depth levels could provide high-quality texture information. Based on this finding, we developed RANKGP-CNN, a method that performs multi-layer feature extraction. More specifically, RANKGP-CNN treats every CNN model as a vast collection of deep composite functions, where each function computes a 2D activation map for every input image. A feature ranking approach then assigns a score to each deep composite function by processing the activation maps generated for a particular dataset bank. Eventually, RauxGP-CNN uses the top-ranked deep composite functions to compute feature vectors for different texture datasets. Experiments on a dedicated classifier showed that RauxGP-CNN achieves good results and can adapt to different texture problems. Finally, we present RauxGP-3M-CNN as the version of RANKGP-CNN that considers multiple CNN models. Overall, RaNKGP-3M-CNN achieves promising results with the advantage of only using the default scale of the input images. (C) 2020 Published by Elsevier Inc.

Automated summary

The study examined the effects of global pooling measurements on extracting texture information, finding that the RANKGP-CNN and RauxGP-CNN methods are effective in extracting high-quality texture information and performing well on different texture problems.