  Several image pattern recognition tasks rely on superpixel generation as a
fundamental step. Image analysis based on superpixels facilitates
domain-specific applications, also speeding up the overall processing time of
the task. Recent superpixel methods have been designed to fit boundary
adherence, usually regulating the size and shape of each superpixel in order to
mitigate the occurrence of undersegmentation failures. Superpixel regularity
and compactness sometimes imposes an excessive number of segments in the image,
which ultimately decreases the efficiency of the final segmentation, specially
in video segmentation. We propose here a novel method to generate superpixels,
called iterative over-segmentation via edge clustering (ISEC), which addresses
the over-segmentation problem from a different perspective in contrast to
recent state-of-the-art approaches. ISEC iteratively clusters edges extracted
from the image objects, providing adaptive superpixels in size, shape and
quantity, while preserving suitable adherence to the real object boundaries.
All this is achieved at a very low computational cost. Experiments show that
ISEC stands out from existing methods, meeting a favorable balance between
segmentation stability and accurate representation of motion discontinuities,
which are features specially suitable to video segmentation.
