Abstract
We propose a probabilistic object classifier for outdoor scene analysis
as a first step in solving the problem of scene context generation.
The method begins with a top-down control, which uses the previously
learned models (appearance and absolute location) to obtain an initial
pixel-level classification. This information provides us the core
of objects, which is used to acquire a more accurate object model.
Therefore, their growing by specific active regions allows us to
obtain an accurate recognition of known regions. Next, a stage of
general segmentation provides the segmentation of unknown regions
by a bottom-strategy. Finally, the last stage tries to perform a
region fusion of known and unknown segmented objects. The result
is both a segmentation of the image and a recognition of each segment
as a given object class or as an unknown segmented object. Furthermore,
experimental results are shown and evaluated to prove the validity
of our proposal.
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