Object-Oriented Model for GIS Compressed Images
Boris Rachev, Mariana Stoeva
Technical University of Varna, Department of Computer Science
1, Studentska str., 9010, Varna, Bulgaria
Abstract
In this paper we analyse the existing approaches to image data modelling and we propose an
Object-Oriented Model for GIS compressed images (OOMCI).
Image databases (IDB) are a very important element of Information Systems, and of GIS and
Multimedia applications in particular. IDB usually require large memory resources and
realization in the computer network environment. Therefore we discuss a proposal for the
structure of the Compressed Image Databases (CIDB) with new effective spatial queries
system.
The main problem of the CIDB design is the structure of the compressed image model (CIM).
It must be extensible and represent the structure and content of the image, its objects and the
relationships between them. We think an appropriate CIM should be a very useful tool for the
search process in this system. In the same time we have no standard model for the semantic
wealth of the image that should be a base for the new CIM invention. However we want to
have a CIM with the content-based image accessible in an environment of compressed
images.
The proposed object-oriented model for the compressed GIS images is based on the logical
representation of the compressed image, that is an abstraction of the real world. We use a
proper method for image compression and Hierarchical Point-to-Point Model (HPPM) of the
Real World for the creation of a new global IDB structure. OOMCI is a universal model and
can be used in the medical & electronic IDB, with the large diversity of images, but GIS are
the main area of its putting into practice.
Keywords: Data Bases, Image & Video Databases, DBMS, Object-Oriented Model, Real
World Model, RGW, GIS.
1. Introduction
Image bases are an essential part of information systems and multimedia applications that
brings their continuous development. The fact that images are much richer in image
information than text and can be differently interpreted according to the application domain
makes image data management very complex.
There are variable and specific types of problems that appear in different applications and as a
result the basic characteristics that must be identified and extracted from the images are very
different.
Image databases require large storage resources and usually a network information access.
Due to this customer server technologies are used mainly. The continuous IDB development
increases their capabilities and improves their basic characteristics – quickness, flexibility and
the essential storage for the data. This imposes IDB design to integrate ideas and techniques
of various areas of computer science such as computer graphics, image processing, image
identification, artificial intelligence, database & knowledge base methodology, and GIS of
course. This composition of ideas culminates in new ideas for representation and new data
models, exact and efficient algorithms for query processing and proper independent systems
of architecture. One solution that decreases the necessary storage is the compressed image
storage or creation of compressed images databases. CIDB storage adds new problems to IDB
design. Image compression has a large application and gives a powerful computer imageprocessing device. Compression methods with and without information loss, of different
generations with high compression degree, based on transformations that imitate human
visual system models are created. Such are the well-known compression methods by outline
intensity, by segmentation and stable and unstable object identification. They use the same
mathematical techniques as the one for information extraction from images. These methods
are of great interest for CIDB authors as they view the image from aspects that support its
logical presentation in the models of images data.
Pictorial data model is one of the main problems in Image database systems (IDBS) design
and development. Data model has to be extensible, to possess an expressive might and to be
able to present image structure and content, the objects it contains and their relationships.
The design of an appropriate data model guarantees the abilities for image search in an IDBS.
The model gets more complicated due to the complexity of image interpretation in
dependence of application area, the lack of a standard model for representation of the
semantic wealth of an image, the desire for images to be stored in a compressed form and the
access approaches to be based on image content. Object oriented models for data
representation have the most wide spread application no matter the IDBMS type – relational
or object oriented. These models allow direct presentation of hierarchical information in its
most natural analogue form, namely data storage in different abstraction levels [8, 10].
2. Definition of the Problem
The experience in the domain of creation and utilization of models for interpretation of real
world objects images (RW) represented somehow in the formal computer world (CW) shows
that a creation of a new model of object oriented type above all, but for representation and
retrieval of compressed images is necessary and possible. Such a model has to support direct
image search at different levels including spatial search. It also has to be applicable in a wide
variety of image collections. The utilization of multiple logical representations of an image is
necessary for this purpose. The model searched has to be employable in the following
application domains: GIS, bases of medical images, image catalogues, etc.
3. Solution of the Problem
3.1. Background: Image description
3.1.1.Classification of the Image Data and its contents
In general, image data can be classified as:
 Non-spatial, alphanumeric data that are attribute based;
 Spatial data consisting of spatial properties of image-objects;
 Graphical data consisting of expressive image characteristics description. They are
closely connected with spatial data.
The image data can be treated as physical image representation and their meaning as logical
representation. The representation includes tools and approaches for description of the image,
image–objects characteristics and their relationships [11].
Physical representation
The physical representation is commonly in raster or vector forms. The raster form includes
the image header and the image matrix. The vector form that is intended mainly for pictorial
images includes mathematical description of image-objects. Compressed images are presented
in their encoded form that depends on the compression algorithm used.
Logical representation
The logical representation includes image description as general, description of image-objects
and their relationships. A detailed review of the tools and approaches to logical representation
is given in [11].
3.1.2. General image description
Images are described by general describing attributes and by attributes extracted from image
content that characterize it as whole. General describing attributes are meta attributes and
semantic attributes. Meta attributes refer to the process of image creation. Semantic
attributes contain subjective information about the image. A specialist in IDB application
domain gives the values of these attributes. The attributes that are extracted from the image
are colour and structural attributes. Colour attributes may be histograms of intensity of the
contours’ colours, average basic colours, overall average colour, etc. Structural attributes are
extracted by either structural methods, identifying the structure primitives or statistical
methods using spatial distribution of image pixels’ intensity.
The objects are separated and associated with the corresponding names after image
processing. As general this process is named segmentation. The determination of the objects
of interest is made by different segmentation techniques such as: threshold, texture, special
images, contour and contour-segment methods, objects identification, mouse drawings, etc.
3.1.3. Object description
Object description uses semantic, logical, colour, texture and shape attributes. Semantic
attributes describe image objects characteristics subjectively. Logical attributes are obtained
as metric characteristics of image- objects such as: height, width, diameter, perimeter, area,
angles, etc. Colour objects attributes are the same as these of the image. Textural objects
attributes are coarseness, contrast, directionality, regularity and roughness. Objects shape
describing attributes take into account boundary based geometrical methods (lists of corner
point and chain codes, minimum boundary rectangle [7, 9]), geometrical or structural
regions based methods on spatial domains ( primitive and 2-D strings [9]), region based on
domain transformation methods.
3.1.4. Objects relationships representation
Spatial relations are the mast commonly used. They are represented in the logical structures
for spatial data presentation and are described by specific alphanumeric strings. The chosen
spatial data representation structure is definite for the efficient spatial query processing.
Object oriented image data structure makes IDBMS more flexible, intelligent and faster and
allows the knowledge embedded in images to be captured by the data structure as much as
possible, especially spatial knowledge [9]. Structures are point structures and extended spatial
domains.
Point structures are intended to present data points in multidimensional space. Such are Btrees, binary trees, point quad trees and region quad trees, Kd –tree (k-dimensional binary
tree), K-D-B-tree, consisting of region and point pages.
Data structures for extended spatial areas are continuously developing and aiming efficient
storage use and easy information retrieval. There are data structures using minimum boundary
rectangles (MBR) and orthogonal relations, corner stitching, cell and grid models, 4D-tree, Rtree (multidimensional generalization of the B-tree), R+ -tree and K-D-B- tree (proposed to
overcome the problem of overlapping MBR of the R-tree and the dead space of the R+-tree).
All structures have their advantages and defects. Their knowledge may be used as a guide for
new data structures design and for comparison with qualitative new structures.
Description of the spatial relations among the objects in the logical data structure is made
most commonly by strings of the following types: (1) 2-D string describing orthogonal
relations among the objects, represented by MBR and their centroids; (2) R-string describing
objects’ MBRs centroids order along a radial bounding line that begins in the image centroid
and makes a full turn counter clockwise. For abstract information representation string
grammar, indefinite grammar and predicative logic are used as well as for presentation of
description tools for spatially oriented completely connected graph, where each edge’s
weight is the slope of the line that connects the MBR’s centroids of the corresponding
objects. Matrixes are used for representation of the following relations: topologic relation of
the type intersect touch, not intersect and not touch; vector relations presenting the relative
position of the objects according to the four cardinal points; metric relations, giving the
distances between the objects (near, far, too near, too far).
3.2. CIDB Data Models
The idea for image memory as a compressed image is too attractive for the researchers due to
the great storage necessary for IDB and at the same time it sets new problems for the design
[4]. High range compression algorithms, based on transformations that imitate human vision
system models appeared the last ten years. These are the compression methods by contour
intensity, by segmentation and by stable and unstable object identification [2, 6]. As well as
in image analysis, so in the compression the way of data representation for the particular
pixels is very important. In the filtration as a stage of the analysis should be operated directly
with the image intensity values. In more complex compression associated assignments it is
better to operate with other data representation forms in higher level using primitives. What
exactly these primitives should be is still a permanent research domain. Most commonly they
are vectors describing particular class image information. The primitive is a semantic and
significant image characteristic [6]. Such a primitive is proposed for processing and
compressing by the image contours intensity. In other segmentation image compression
methods segmentation is applied to homogeneous areas, surrounded by contours that are
encoded. Objects identification based methods involve preliminary description of the objects
that may appear. These methods are of specific interest for CIDB designers as they view the
image in aspects that support its logical representation in the image data models.
In dependence of image data application and the search level one or a compression method
should be preferred, retaining or not the images semantics [15].
The specific character of image collection application predetermines the type of the models in
the existing CIDB. They support direct search of the image content base at different levels.
 At the lower image search level the global, general image characteristics should be
used, specified by its semantic, meta, colour and texture attributes, when no context
information and no area specification are required.
 At the next search level the image objects typical features should be used specified by
its semantic, logical, colour, texture and shape attributes.
 The spatial search on the basis of the topological, vector, matrix and spatial
relationships is at the higher level.
A basic challenge for researchers is the creation of data model structures that support this
higher-level search. The most popular IDBMS is IIDS (Intelligent Image Database System),
where images are stored compressed as a whole. The IIDS model supports spatial data, their
flexible retrieval, visual representation and the traditional operations on IDB [9]. In this model
the spatial data structure is represented by 2-D strings and gives an efficient tool for iconic
indexing in DBMS and spatial argumentation. The 2-D string is especially popular because it
is very efficient in describing symbol pictures and describes the orthogonal relations between
the objects determined in their MBRs meaning.
3.3. Object-Oriented Model for Compressed Images – General description
The proposed model is built after analysing the existing tools and approaches for image data
modelling, the image compression algorithms and the existing data models. The objectoriented model for image data representation was preferred. It detects naturally the
inherent image data structure since an object may be created at different levels of spatial
division and also at different levels of hierarchic description and view of the real world, for
example by the HPPM mode [13] and its application [14]. The object-oriented model
increases the data structuring flexibility and allows presentation of great quantity of semantic
information [1].
PHYSICAL
IMAGE
REPRESENTATION
LOGICAL
IMAGE REPRESENTATION
IMAGE
Image
Compression
Codes
DIGITIZED
IMAGE
Image
Colour
Attributes
Image
Meta
Attributes
Image
Texture
Attributes
Image
Semantic
Attributes
1st level
Object
Compression
Codes
SEGMENTED
IMAGE
Object
Colour
Attributes
Object
Texture
Attributes
Object
Shape
Attributes
Object
Semantic
Attributes
SEGMENTATION
SYMBOLIZED
IMAGE
Object
Logical
Attributes
2nd level
Spatial
Object
Attributes
3rd
level
Legend
Compression
Content-based attributes
Figure
RELATION
1: OOMCI Structure
Symbol attributes
The model supports spatial data and allows determination of the relationships between
alphanumeric and spatial data, represented by primitive entities named objects (areas as
points, lines and segments) of different type. The fact that any spatial entity with a definite
shape can be presented as an object is taken into account. The objects are an entity that
combines at the same time the processing properties and the data, the classes are premeditated
data description and the different examples of a given class form the extension data [8]. The
one-by-one connection appears at different levels. The model supports traditional operations
on CIDB and flexible image retrieval.
The conceptual images OOMCI representation takes place by concepts describing images
structure (Figure 1).
Three forms of image are used: numeric, segmented and symbolized. The segmented image
is obtained from the previous numeric image by segmentation. It contains the objects
identified in the image. The symbol image describing the relations between the objects is
obtained from the segmented image by description the spatial relationships between the
objects according to the chosen data storage structure. It is represented by specific strings,
matrixes or double-linked lists.
The image is represented in two aspects, physical and logical.
The physical representation is determined at:
 The lower search level of the description of the code records that contain the images as
a whole in compressed form.
 The next search level of the description of the code records, containing the object in
compressed form.
 The compressed records description depends on the compression algorithm. At the
lower level this may be anyone of the existing compression methods and its choice is
determined by the compression coefficient it achieves. At the next level for object
storage in compressed form an algorithm has to be used, that takes an account of
imitating human visual system model corresponding to the logical objects
representation.
The logical representation of a given image is obtained from the defined general image
description, the objects description and the objects relationships description. This form of
information representation allows image indexing and retrieval at different search levels, in
different in type characteristics.
The general description is obtained by extracting the general image characteristics from the
numeric image. It includes a description of image general characteristics represented by meta
and semantic attributes and a description of the characteristics based on the information
content and represented by colour and texture attributes. Most present IDB use this image
description method. The general image description supports direct search at lower level in
attributes describing the image as a whole.
Another way of image description is by its visual content, namely objects description. Objects
description is obtained by extracting the objects and their characteristics from the segmented
image. It includes a description of objects general characteristics represented by logical and
semantic attributes and a description of characteristics based on objects content represented
by colour, texture and form attributes. This description supports next level search and assume
the existence of some previous knowledge about the values of these object characteristics.
The object code record that represents it in compressed form may be used as form attribute
when the compressed algorithm is appropriately chosen. Some present IDB use these very
form attributes.
Objects description may be extended by description of the relationships among them. The
description of the relationships among the objects is obtained from the symbol description of
the relationships among the objects. Objects attributes for spatial search are extracted by
corresponding algorithms. Spatial query processing requires description of the relationships
among the objects. Spatial relations are presented in different data structures and their
description depends on the chosen structure. The most commonly used approach is the
description by string or double linked lists. Matrixes are used for description of topological,
metric and vector relations. The objects attributes supporting spatial search are extracted from
the symbol representation by appropriate algorithms.
IMAGE
IMAGE ATTRIBUTES
Image
Compression
Codes
Colour
Histogram
RGB model
46%,28%,25%
Meta
Name –Varna/l/33
Date - 01/01/99
Source- reg11, page
32
Texture
Contrast 0.84
Semantic
Region –Varna
Coordinates –
430 8’6”
0/0(EOB) 1010
0/1
0000
0/2
0101
0/3
1011
1st level
SEGMENTED IMAGE
IMAGE
H
P PR
OBJECTS
G
H
P
Object H
Object
P
Compression
Object
R
Object
G
Codes
Compression
Compression
0100101….. Compression
Codes
0100101
…..…..
Codes
0100101
Codes
0100101…..
R
G
ATTRIBUTES
Colour
Average
purple
Texture
contrast
Shape
Contour
2064242
464
Semantic
Type - hospital
Owner - Petrow
Logical
Area
350 m2
Colour
Average
blue
Texture
Contrast
0.76
Shape
Contour
2107654
3
Semantic
Type - pond
Owner-Petrow
Logical
Area
25m2
Colour
Average
grey
Texture
Contrast
0.35
Shape
Contour
2710531
3
Semantic
Type - road
Owner-municipality
Logical
Area
115 m2
Colour
Average
green
Texture
Contrast
1.25
Shape
Contour
1753
Semantic
Type -garden
Owner-municipality
Logical
Area
500 m2
1.98
2nd level
Figure 2 a: OOMCI hierarchical (1st and 2nd levels) modelling of the GIS compressed image
YY
MBR
H
(53,130) (85,127)
P
G
(123,125)
(45,120)
R
X
X
ORTHOGONAL
RELATION
R3
R1
H1
RELEVANT
POSITION
OR
G1
H
R
P
H
2
R2
G
G2
G1
H1
R string
(R, H, P, G)
SPATIAL
INDEX
R1
P
H
2 R2
G2
R3
SYMBOLIZED
IMAGE
2-D string
((H1 P<H2<R1<R3<R2<G2<G1; P H2 R2) G2<R3<G1<H1 R1)
SPATIAL
INDEX
3rd level
Figure 2 b: OOMCI hierarchical (3rd level) modelling of the GIS compressed image
3.4. Object-Oriented Model for Compressed Images - Example
An example of image OOMCI description of a built-up area district draft is shown in Fig. 2.
4. Implementation of the OOMCI on the HPPM images
The proposed OOMCI may be used over primarily created images in accordance with the
hierarchic architecture of their data obtained over HPPM model. In this case its general
mathematical description will be as follows:
w = OOGw(Pij,Rij),
where:
OOG is an Object-Oriented variant of the HPPM Model, j=1, Ni, i=1, K and Rij are the
spatial or simple relationships of the Points Pij. Each set of Points Pij on each level i must be
used as a basis for the image representation and its implementation by OOMCI. Here j is the
index of the number of image points, which are situated on the level i.
The first level of the OOMCI model over HPPM hierarchic representation of realistic image
of a part of the real world is illustrated in Figure 3. This is the level of a hierarchic set of
images. Each of them may be represented at the next level of the object-oriented model in
accordance with the schemes in Figures 1 and 2.
IMAGE 1
Image
Compressed
Codes
….. 0011
One HPPM
Point and one
OOMCI
representation at level
∞
IMAGE 1 ATTRIBUTES
Color
Semantic
Texture
-
Meta
Spatial HPPM
Relationships “Oneto-many”
Two HPPM Sets of Points
and two OOMCI
representation at level i+1
…
.
Two HPPM Points at
One Full OOMCI
the level i
representation at level i
Image
Compressed
Codes
….. 01100
Color
IMAGE 2
Semantic
Meta
Texture
IMAGE 2 ATTRIBUTES
1st level
Figure 3: 1st level of the OOMCI implementation example for the HPPM hierarchical series of images
5. Conclusions
The proposed model generalized the experience of the existing image data models allowing
storage in compressed form. It is used for image representation and retrieval and it is:
 Applicable for a great number of collections;
 Flexible and may be conformed to the application specificity;
 Supporting direct search not only according to alphanumeric attributes, but also
according to characteristics extracted from the image at different search levels – general
image characteristics, object characteristics and spatial characteristics;
 Allowing different types of functions on the physical and logical image representation.
The CIDB development is directed to: search of algorithms for automatic extraction of data
characteristics from the images, composition of structures for spatial data representation and
retrieval, improvement of structures description approaches.
6. Acknowledgments
This work is supported by the INCO Copernicus Project URBAN 960252 and includes some
proposals, which develop the results of this one.
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