  Procedural textures are normally generated from mathematical models with
parameters carefully selected by experienced users. However, for naive users,
the intuitive way to obtain a desired texture is to provide semantic
descriptions such as "regular," "lacelike," and "repetitive" and then a
procedural model with proper parameters will be automatically suggested to
generate the corresponding textures. By contrast, it is less practical for
users to learn mathematical models and tune parameters based on multiple
examinations of large numbers of generated textures. In this study, we propose
a novel framework that generates procedural textures according to user-defined
semantic descriptions, and we establish a mapping between procedural models and
semantic texture descriptions. First, based on a vocabulary of semantic
attributes collected from psychophysical experiments, a multi-label learning
method is employed to annotate a large number of textures with semantic
attributes to form a semantic procedural texture dataset. Then, we derive a low
dimensional semantic space in which the semantic descriptions can be separated
from one other. Finally, given a set of semantic descriptions, the diverse
properties of the samples in the semantic space can lead the framework to find
an appropriate generation model that uses appropriate parameters to produce a
desired texture. The experimental results show that the proposed framework is
effective and that the generated textures closely correlate with the input
semantic descriptions.
