Visualizing and Understanding Deep Texture Representations

Understand

A number of recent approaches have used deep convolutional neural networks (CNNs) to build texture representations.

• Nevertheless, it is still unclear how these models represent texture and invariances to categorical variations.
• This work conducts a systematic evaluation of recent CNN-based texture descriptors for recognition and attempts to understand the nature of invariances captured by these representations.
• First we show that the recently proposed bilinear CNN model [25] is an excellent general-purpose texture descriptor and compares favorably to other CNN-based descriptors on various texture and scene recognition benchmarks.