Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 66-69The common goal of object-based image analysis techniques in the literature
is to partition the images into homogeneous regions and classify these regions.
However, such homogeneous regions often correspond to very small details in very
high spatial resolution images obtained from the new generation sensors. One
interesting way of enabling the high-level understanding of the image content is
to identify the image regions that are intrinsically heterogeneous. These image
regions are comprised of primitive objects of many diverse types, and can also be
referred to as compound structures. The detection of compound structures can
be posed as a generalized segmentation or generalized texture detection problem,
where the elements of interest are primitive objects instead of traditional case of
pixels. Traditional segmentation methods extract regions with similar spectral
content and texture models assume specific scale and orientation. Hence, they
cannot handle the complexity of compound structures that consist of multiple
regions with different spectral content and arbitrary scale and orientation.
In this thesis, we present an unsupervised method for discovering compound
image structures that are comprised of simpler primitive objects. An initial segmentation
step produces image regions with homogeneous spectral content. Then,
the segmentation is translated into a relational graph structure whose nodes correspond
to the regions and the edges represent the relationships between these
regions. We assume that the region objects that appear together frequently can
be considered as strongly related. This relation is modeled using the transition
frequencies between neighboring regions, and the significant relations are found as
the modes of a probability distribution estimated using the features of these transitions.
Furthermore, we expect that subgraphs that consist of groups of strongly
related regions correspond to compound structures. Therefore, we employ two
different procedures to discover the subgraphs in the constructed graph. During
the first procedure the graph is discretized and a graph-based knowledge discovery
algorithm is applied to find the repeating subgraphs. Even though a single
subgraph does not exclusively correspond to a particular compound structure,
different subgraphs constitute parts of different compound structures. Hence,
we discover compound structures by clustering the histograms of the subgraph
instances with sliding image windows. The second procedure involves graph segmentation
by using normalized cuts. Since the distribution of significant relations
within resulting subgraphs gives an idea about the nature of corresponding compound
structure, the subgraphs are further grouped by clustering the histograms
of the most significant relations.
The proposed method was tested using an Ikonos image. Experiments show
that the discovered image areas correspond to different high-level structures with
heterogeneous content such as dense residential areas with high buildings, dense
and sparse residential areas with low height buildings and fields.Zamalieva, DaniyaM.S
