Abstract:
The current ubiquity of digital image capturing systems has prompted a flurry of
research activities aimed at the development of sophisticated content-based image
data management techniques. The fundamental ingredient of content-based image
retrieval is the selection of appropriate features to describe the content of the
image. Shape of an object, represented by its contour, is the most important visual
feature that is thought to be used by humans to determine the similarity of objects.
The selected feature and its distance measure must be robust to different
distortions such as noise, orientation, scale and rotation. In this thesis, we present
an effective representation of shape, using its boundary information that is robust
to arbitrary distortions and affine transformation. The contour representation of
shape is converted into time series and then critical points are extracted from the
time series using k-beam mechanism. The time series is partitioned between two
consecutive critical points to facilitate partial matching. Partitions of time series
are modeled using Discrete Fourier Transformation (DFT). Shape matching is
then carried out in real space using Dynamic Time Warping. Angle between two
consecutive critical points is calculated, compared during matching of shapes,
and this leads to efficiency gains over existing approaches. Experimental
evaluation demonstrates that the proposed shape representation and matching
mechanism is effective, efficient and robust to different arbitrary and affine
distortions.
