Deep residual pooling network for texture recognition

Highlights：

• We propose a learnable residual pooling layer comprising of a residual encoding module and an aggregation module that retains spatial information and aggregates them to a feature with a lower dimension.

• We propose an end-to-end learning framework that integrates the residual pooling layer into any pre-trained CNN model for efficient feature transfer for texture recognition.

• We compare the performance of the proposed pooling layer with other residual encoding schemes to illustrate state-of-the-art performance on benchmark texture datasets, an industry dataset and a scene recognition dataset.