  Textural and structural features can be regraded as "two-view" feature sets.
Inspired by the recent progress in multi-view learning, we propose a novel
two-view classification method that models each feature set and optimizes the
process of merging these views efficiently. Examples of implementation of this
approach in classification of real-world data are presented, with special
emphasis on medical images. We firstly decompose fully-textured images into two
layers of representation, corresponding to natural stochastic textures (NST)
and structural layer, respectively. The structural, edge-and-curve-type,
information is mostly represented by the local spatial phase, whereas, the pure
NST has random phase and is characterized by Gaussianity and self-similarity.
Therefore, the NST is modeled by the 2D self-similar process, fractional
Brownian motion (fBm). The Hurst parameter, characteristic of fBm, specifies
the roughness or irregularity of the texture. This leads us to its estimation
and implementation along other features extracted from the structure layer, to
build the "two-view" features sets used in our classification scheme. A shallow
neural net (NN) is exploited to execute the process of merging these feature
sets, in a straightforward and efficient manner.
