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Every node in the graph corresponds to a single
image object, that is, one or multiple connected re-
gions in the image. Image objects can arbitrarily span
across the spatial dimensions of the image. For each
image object, there has to exist a unique mapping to
the corresponding pixels in the image. In the most
basic case, this mapping is established through a la-
beled image in which pixels from one object store a
common integer value identifying the object. In ad-
dition to this kind of direct mapping, objects can be
mapped indirectly through directly mapping objects
which constitute their parts.
Edges in the attributed relational graph represent
contextual relations between image objects, such as
adjacency, overlap and parthood. While the mean-
ing of relations is typically subject to the application,
there is a set of generally defined relations that are
commonly used. Examples of these are neighbor re-
lations between image objects that share a common
border and part of relations between indirectly and
directly mapped objects.
Both image objects and relations can be associated
with numeric and nominal attributes. For image ob-
jects, such attributes typically store features like their
size, shape, intensity statistics, orclassification labels.
For relations, they store inherent parameters, like the
border length between adjacent objects (see Figure 1).
2.1 Persistence and Query
For real world applications, it is essential to have
means of making the data model persistent and to
query subsets of its content. Attributed relational
graphs can naturally be expressed in terms of the rela-
tional data model (Chang and Fu, 1979)—the founda-
tion of all relational databases. Relational databases
have the favorable properties of being well estab-
lished, scalable beyond the limits of working memory
and to enable fast and complex queries on the basis of
SQL. On this account, the presented concept relies on
relational databases for data management.
Since it turned out to be difficult to devise a gen-
eral database schema for a broad range of applica-
tions, the database layout is left to be adapted specif-
ically to the application. However, for reasons of in-
teroperability and clarity, the database layout is ex-
pected to comply to the following conventions. The
presented concept distinguishes between three types
of database tables for objects, relations and attributes.
Object tables store the identifiers of image objects
with a common mapping. Accordingly, object tables
contain only a single integer column “id”. Directly
mapped image objects, that is, objects whose identi-
fiers correspond to pixels values in the labeled image,
are stored in a special “base objects” table. Indirectly
mapped image objects are stored together when they
belong together semantically.
Relation tables store a common type of relations
between image objects. They contain two integer
columns “src” and “dest” that store the identifiers of
the source and destination objects joined by one rela-
tion. For undirected relations, the table contains two
entries in opposite directions.
Attribute tables store the attributes of image ob-
jects and relations. For objects attributes, the first col-
umn “id” contains the identifier of the image object.
For relation attributes, the first two columns “src” and
“dest” contain the identifiers of the connected objects.
In addition to these key columns, attribute tables can
have an arbitrary number of integral, real or string
columns that store the values of the actual attributes.
While it would be possible to have one attribute ta-
ble to store the attributes of all objects, it is generally
advised to provide one table per extraction method.
Since most attributes are only computed for subsets
of objects, the distributed storage of attributes reduces
redundancy and, in turn, the size if the database and
computational costs of queries.
2.2 Reasoning
Typically, image analysis requires knowledge on the
objects in an image like their size, shape, intensity
characteristics, or spatial distribution patterns. The
computation of such information is commonly called
“feature extraction” while the evaluation of objects on
the basis of such features is commonly called “classi-
fication”.
The first step in every object-based analysis
method is to derive an initial set of objects. This re-
quires a segmentation of the original image that de-
lineates the basic semantic entities. For this purpose,
common methods like the watershed transformation
(Vincent and Soille, 1991) are feasible. However,
depending on the domain, customized segmentation
methods might also be considered.
After the initial set of objects has been inserted
into the database, all further steps complement the
database with additional attributes, objects or rela-
tions. In the beginning, the calculation of features
and relations helps to group base objects that repre-
sent parts of bigger structures. By merging these ob-
jects, more complex structures can be identified, thus
gaining more contextual information. By extracting
spectral or structural features for the merged objects,
further classifications are possible on which, again,
feature extraction and classification can be performed.
That way, the object-based analysis of images be-
A GENERIC CONCEPT FOR OBJECT-BASED IMAGE ANALYSIS
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