  We introduce a dynamic multiscale tree (DMT) architecture that learns how to
leverage the strengths of different existing classifiers for supervised
multi-label image segmentation. Unlike previous works that simply aggregate or
cascade classifiers for addressing image segmentation and labeling tasks, we
propose to embed strong classifiers into a tree structure that allows
bi-directional flow of information between its classifier nodes to gradually
improve their performances. Our DMT is a generic classification model that
inherently embeds different cascades of classifiers while enhancing learning
transfer between them to boost up their classification accuracies.
Specifically, each node in our DMT can nest a Structured Random Forest (SRF)
classifier or a Bayesian Network (BN) classifier. The proposed SRF-BN DMT
architecture has several appealing properties. First, while SRF operates at a
patch-level (regular image region), BN operates at the super-pixel level
(irregular image region), thereby enabling the DMT to integrate multi-level
image knowledge in the learning process. Second, although BN is powerful in
modeling dependencies between image elements (superpixels, edges) and their
features, the learning of its structure and parameters is challenging. On the
other hand, SRF may fail to accurately detect very irregular object boundaries.
The proposed DMT robustly overcomes these limitations for both classifiers
through the ascending and descending flow of contextual information between
each parent node and its children nodes. Third, we train DMT using different
scales, where we progressively decrease the patch and superpixel sizes as we go
deeper along the tree edges nearing its leaf nodes. Last, DMT demonstrates its
outperformance in comparison to several state-of-the-art segmentation methods
for multi-labeling of brain images with gliomas.
