  Research in texture recognition often concentrates on recognizing textures
with intraclass variations such as illumination, rotation, viewpoint and small
scale changes. In contrast, in real-world applications a change in scale can
have a dramatic impact on texture appearance, to the point of changing
completely from one texture category to another. As a result, texture
variations due to changes in scale are amongst the hardest to handle. In this
work we conduct the first study of classifying textures with extreme variations
in scale. To address this issue, we first propose and then reduce scale
proposals on the basis of dominant texture patterns. Motivated by the
challenges posed by this problem, we propose a new GANet network where we use a
Genetic Algorithm to change the units in the hidden layers during network
training, in order to promote the learning of more informative semantic texture
patterns. Finally, we adopt a FVCNN (Fisher Vector pooling of a Convolutional
Neural Network filter bank) feature encoder for global texture representation.
  Because extreme scale variations are not necessarily present in most standard
texture databases, to support the proposed extreme-scale aspects of texture
understanding we are developing a new dataset, the Extreme Scale Variation
Textures (ESVaT), to test the performance of our framework. It is demonstrated
that the proposed framework significantly outperforms gold-standard texture
features by more than 10% on ESVaT. We also test the performance of our
proposed approach on the KTHTIPS2b and OS datasets and a further dataset
synthetically derived from Forrest, showing superior performance compared to
the state of the art.
