Abstract
The traditional processing flow of segmentation followed by classification
in computer vision assumes that the segmentation is able to successfully
extract the object of interest from the background image. It is extremely
difficult to obtain a reliable segmentation without any prior knowledge
about the object that is being extracted from the scene. This is
further complicated by the lack of any clearly defined metrics for
evaluating the quality of segmentation or for comparing segmentation
algorithms. We propose a method of segmentation that addresses both
of these issues, by using the object classification subsystem as
an integral part of the segmentation. This will provide contextual
information regarding the objects to be segmented, as well as allow
us to use the probability of correct classification as a metric to
determine the quality of the segmentation. We view traditional segmentation
as a filter operating on the image that is independent of the classifier,
much like the filter methods for feature selection. We propose a
new paradigm for segmentation and classification that follows the
wrapper methods of feature selection. Our method wraps the segmentation
and classification together, and uses the classification accuracy
as the metric to determine the best segmentation. By using shape
as the classification feature, we are able to develop a segmentation
algorithm that relaxes the requirement that the object of interest
to be segmented must be homogeneous in some low-level image parameter,
such as texture, color, or grayscale. This represents an improvement
over other segmentation methods that have used classification information
only to modify the segmenter parameters, since these algorithms still
require an underlying homogeneity in some parameter space. Rather
than considering our method as, yet, another segmentation algorithm,
we propose that our wrapper method can be considered as an image
segmentation framework, within which existing image segmentation
algorithms may be executed. We show the performance of our proposed
wrapper-based segmenter on real-world and complex images of automotive
vehicle occupants for the purpose of recognizing infants on the passenger
seat and disabling the vehicle airbag. This is an interesting application
for testing t- he robustness of our approach, due to the complexity
of the images, and, consequently, we believe the algorithm will be
suitable for many other real-world applications.
- airbag,
- analysis,
- approach
- automotive
- classification
- classification,
- colour
- computer
- extraction,
- feature
- image
- infant
- object
- occupants,
- passenger
- probability
- recognition,
- seat,
- segmentation,
- selection,
- subsystem,
- texture,
- vehicle
- vision,
- wrapper-based
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