1 research outputs found
Improved Image Segmentation via Cost Minimization of Multiple Hypotheses
Image segmentation is an important component of many image understanding
systems. It aims to group pixels in a spatially and perceptually coherent
manner. Typically, these algorithms have a collection of parameters that
control the degree of over-segmentation produced. It still remains a challenge
to properly select such parameters for human-like perceptual grouping. In this
work, we exploit the diversity of segments produced by different choices of
parameters. We scan the segmentation parameter space and generate a collection
of image segmentation hypotheses (from highly over-segmented to
under-segmented). These are fed into a cost minimization framework that
produces the final segmentation by selecting segments that: (1) better describe
the natural contours of the image, and (2) are more stable and persistent among
all the segmentation hypotheses. We compare our algorithm's performance with
state-of-the-art algorithms, showing that we can achieve improved results. We
also show that our framework is robust to the choice of segmentation kernel
that produces the initial set of hypotheses.Comment: Accepted BMVC 1