Learning to segment images using dynamic feature binding
Despite the fact that complex visual scenes contain multiple, overlapping
objects, people perform object recognition with ease and accuracy. One
operation that facilitates recognition is an early segmentation process
in which features of objects are grouped and labeled according to which object
they belong. Current computational systems that perform this operation are
based on predefined grouping heuristics. We describe a system called
MAGIC that learns how to group features based on a set of presegmented
examples. In many cases, MAGIC discovers grouping heuristics similar to
those previously proposed, but it also has the capability of finding
nonintuitive structural regularities in images. Grouping is performed by a
relaxation network that attempts to dynamically bind related features.
Features transmit a complex-valued signal (amplitude and phase) to one
another; binding can thus be represented by phase locking related features.
MAGIC's training procedure is a generalization of recurrent back propagation
to complex-valued units.
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