Ricerc@Sapienza

Element-based textures are a kind of texture formed by nameable elements, the
texels [1], distributed according to specific statistical distributions; it is
of primary importance in many sectors, namely textile, fashion and interior
design industry. State-of-theart texture descriptors fail to properly
characterize element-based texture, so we present Texel-Att to fill this gap.
Texel-Att is the first fine-grained, attribute-based representation and
classification framework for element-based textures. It first individuates
texels, characterizing them with individual attributes; subsequently, texels
are grouped and characterized through layout attributes, which give the
Texel-Att representation. Texels are detected by a Mask-RCNN, trained on a
brand-new element-based texture dataset, ElBa, containing 30K texture images
with 3M fully-annotated texels. Examples of individual and layout attributes
are exhibited to give a glimpse on the level of achievable graininess. In the
experiments, we present detection results to show that texels can be precisely
individuated, even on textures "in the wild"; to this sake, we individuate the
element-based classes of the Describable Texture Dataset (DTD), where almost
900K texels have been manually annotated, leading to the Element-based DTD
(E-DTD). Subsequently, classification and ranking results demonstrate the
expressivity of Texel-Att on ElBa and E-DTD, overcoming the alternative
features and relative attributes, doubling the best performance in some cases;
finally, we report interactive search results on ElBa and E-DTD: with Texel-Att
on the E-DTD dataset we are able to individuate within 10 iterations the
desired texture in the 90% of cases, against the 71% obtained with a
combination of the finest existing attributes so far. Dataset and code is
available at https://github.com/godimarcovr/Texel-Att