  Dynamic texture (DT) segmentation, and video processing in general, is
currently widely dominated by methods based on deep neural networks that
require the deployment of a large number of layers. Although this parametric
approach has shown superior performances for the dynamic texture segmentation,
all current deep learning methods suffer from a significant main weakness
related to the lack of a sufficient reference annotation to train models and to
make them functional. This study explores the unsupervised segmentation
approach that can be used in the absence of training data to segment new
videos. We present an effective unsupervised learning consensus model for the
segmentation of dynamic texture (ULCM). This model is designed to merge
different segmentation maps that contain multiple and weak quality regions in
order to achieve a more accurate final result of segmentation. The diverse
labeling fields required for the combination process are obtained by a
simplified grouping scheme applied to an input video (on the basis of a three
orthogonal planes: xy, yt and xt). In the proposed model, the set of values of
the requantized local binary patterns (LBP) histogram around the pixel to be
classified are used as features which represent both the spatial and temporal
information replicated in the video. Experiments conducted on the challenging
SynthDB dataset show that, contrary to current dynamic texture segmentation
approaches that either require parameter estimation or a training step, ULCM is
significantly faster, easier to code, simple and has limited parameters.
Further qualitative experiments based on the YUP++ dataset prove the
efficiently and competitively of the ULCM.
