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Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have
been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic,
robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to
computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered
semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical
layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges
the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through
utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and
used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano-
Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on
low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region
dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local
multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient
neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been
conducted within the maritime image environment where the segmented layered semantic objects include the basic level
objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the
proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects/regions
with contextual topological relationships.
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