  In this paper, we propose a multi-level texture encoding and representation
network (MuLTER) for texture-related applications. Based on a multi-level
pooling architecture, the MuLTER network simultaneously leverages low- and
high-level features to maintain both texture details and spatial information.
Such a pooling architecture involves few extra parameters and keeps feature
dimensions fixed despite of the changes of image sizes. In comparison with
state-of-the-art texture descriptors, the MuLTER network yields higher
recognition accuracy on typical texture datasets such as MINC-2500 and
GTOS-mobile with a discriminative and compact representation. In addition, we
analyze the impact of combining features from different levels, which supports
our claim that the fusion of multi-level features efficiently enhances
recognition performance. Our source code will be published on GitHub
(https://github.com/olivesgatech).
