Reinforced Segmentation of Images Containing One Object of Interest
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Date
2007-11-09T18:53:32Z
Authors
Sahba, Farhang
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
In many image-processing applications, one object of interest must
be segmented. The techniques used for segmentation vary depending
on the particular situation and the specifications of the problem
at hand. In methods that rely on a learning process, the lack of a
sufficient number of training samples is usually an obstacle,
especially when the samples need to be manually prepared by an
expert. The performance of some other methods may suffer from
frequent user interactions to determine the critical segmentation
parameters. Also, none of the existing approaches use online
(permanent) feedback, from the user, in order to evaluate the
generated results. Considering the above factors, a new
multi-stage image segmentation system, based on Reinforcement
Learning (RL) is introduced as the main contribution of this
research. In this system, the RL agent takes specific actions,
such as changing the tasks parameters, to modify the quality of
the segmented image. The approach starts with a limited number of
training samples and improves its performance in the course of
time. In this system, the expert knowledge is continuously
incorporated to increase the segmentation capabilities of the
method. Learning occurs based on interactions with an offline
simulation environment, and later online through interactions with
the user. The offline mode is performed using a limited number of
manually segmented samples, to provide the segmentation agent with
basic information about the application domain. After this mode,
the agent can choose the appropriate parameter values for
different processing tasks, based on its accumulated knowledge.
The online mode, consequently, guarantees that the system is
continuously training and can increase its accuracy, the more the
user works with it. During this mode, the agent captures the user
preferences and learns how it must change the segmentation
parameters, so that the best result is achieved. By using these
two learning modes, the RL agent allows us to optimally recognize
the decisive parameters for the entire segmentation process.
Description
Keywords
Image Segmentation