ISSN : 1796-217X
Volume : 3    Issue : 2    Date : February 2008

Hierarchical Image Segmentation by Structural Content
Nathir A. Rawashdeh, Shaun T. Love, and Kevin D. Donohue
Page(s): 41-51
Full Text:
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Image quality loss resulting from artifacts depends on the nature and strength of the artifacts as
well as the context or background in which they occur. In order to include the impact of image context
in assessing artifact contribution to quality loss, regions must first be classified into general
categories that have distinct effects on the subjective impact of the particular artifact. These effects
can then be quantified to scale the artifact in a perceptually meaningful way. This paper formulates
general context categories, develops automatic image region classifiers, and evaluates the
classifier performance using images containing multiple categories. Linear classifiers are
designed to identify three main classes which include random, textured, and transient regions.
Features for identifying these areas over regions at multiple resolutions are based on the optical
density histogram (ODH), the cortex transform, and the cooccurrence matrix. It was found that
selecting features from the ODH and cortex transform provides classification results in agreement
with human assessment, and performances comparable to those of classifiers using cooccurrence
matrix features. Experiments to assess performance show misclassification rates ranging from
3.3% for the lowest resolutions to 32.2% at highest. This paper also presents a hierarchical
classification algorithm that combines classifiers operating at multiple resolutions and achieves an
overall misclassification rate as low as 4.8%.

Index Terms
hierarchical classifier, classification confidence, image structure, image quality, image
segmentation, cortex transform