  Texture-based classification solutions have proven their significance in many
domains, from industrial inspections to health-related applications. New
methods have been developed based on texture feature learning and CNN-based
architectures to address computer vision use cases for images with rich
texture-based features. In recent years, architectures solving texture-based
classification problems and demonstrating state-of-the-art results have
emerged. Yet, one limitation of these approaches is that they cannot claim to
be suitable for all types of image texture patterns. Each technique has an
advantage for a specific texture type only. To address this shortcoming, we
propose a framework that combines more than one texture-based techniques
together, uniquely, with a CNN backbone to extract the most relevant texture
features. This enables the model to be trained in a self-selective manner and
produce improved results over current published benchmarks -- with almost same
number of model parameters. Our proposed framework works well on most texture
types simultaneously and allows flexibility for additional texture-based
methods to be accommodated to achieve better results than existing
architectures. In this work, firstly, we present an analysis on the relative
importance of existing techniques when used alone and in combination with other
TE methods on benchmark datasets. Secondly, we show that Global Average Pooling
which represents the spatial information -- is of less significance in
comparison to the TE method(s) applied in the network while training for
texture-based classification tasks. Finally, we present state-of-the-art
results for several texture-based benchmark datasets by combining three
existing texture-based techniques using our proposed framework.
