  Capturing the essence of a textile image in a robust way is important to
retrieve it in a large repository, especially if it has been acquired in the
wild (by taking a photo of the textile of interest). In this paper we show that
a texel-based representation fits well with this task. In particular, we refer
to Texel-Att, a recent texel-based descriptor which has shown to capture fine
grained variations of a texture, for retrieval purposes. After a brief
explanation of Texel-Att, we will show in our experiments that this descriptor
is robust to distortions resulting from acquisitions in the wild by setting up
an experiment in which textures from the ElBa (an Element-Based texture
dataset) are artificially distorted and then used to retrieve the original
image. We compare our approach with existing descriptors using a simple ranking
framework based on distance functions. Results show that even under extreme
conditions (such as a down-sampling with a factor of 10), we perform better
than alternative approaches.
