More and more images have been created in digital form around the world. There
is growing interest in finding images from a large collection or from remote
databases. In order to find an image, the image has to be described or represented
by certain feature. Shape is an important visual feature of image. Searching
images using shape features has attracted much attention. There are many shape
representation and description techniques in the literature.
In this paper, important shape representation techniques are classified and
reviewed. The implementation procedures of each technique are examined and its
advantage and disadvantage are discussed. Some recent research results have
been included and discussed in the paper. In the end, promising techniques for
image retrieval will be identified according to standard principles.
Generally, there are two classes of approaches in shape representation and
description: contour-based versus region-based. Under each class, the methods
can be divided into structural methods and global methods. The different methods
can be further distinguished between methods working in space domain and methods
working in transform domain.
The review has found that structural approaches are useful in applications
where partial matching is needed; methods based on Hausdorff distance are useful
for locating objects in an image or sub-image matching. Both types of the methods
have limited applications. For general shape applications, methods based on
complex moments and spectral transform, such as Zernike moments and GFD, are
the best choices. They satisfy the six principle set by MPEG-7: good retrieval
accuracy, compact features, general application, low computation complexity,
robust retrieval performance and hierarchical coarse to fine representation.
If storage is a concern, FD can be considered.