LINGUISTIC SUPPORT OF THE KNOWLEDGE BASE FOR
IMAGE ANALYSIS AND UNDERSTANDING SYSTEM
Yulia Trusova, Igor Gurevich, Victor Beloozerov and Dmitri Murashov
Dorodnicyn Computing Center, Russian Academy of Sciences
40 Vavilov str.,119991 Moscow, Russian Federation
Keywords: Image analysis, pattern recognition, knowledge bases, domain thesauri, ontologies, knowledge portals.
Abstract: The problem of lexical and semantic support of the knowledge base for the system for automation of
scientific research in image processing, analysis and understanding is discussed. The main contribution is
the image analysis thesaurus which has been developed as a main tool for solving this problem. A structure
of the thesaurus and functional characteristics of the basic version of the thesaurus are described. Lexical
categories of terms and relationships between terms in the domain of image processing, analysis and
recognition are considered. The thesaurus was implemented as an autonomous program module. The
description of the thesaurus module and its use are provided. The developed thesaurus was applied for
automation of early diagnosis of hematological diseases on the base of cytological specimens.
1 INTRODUCTION
The problem of scientific research automation in the
subject domain of image processing, analysis and
understanding is one of the fundamental problems of
computer science. The paper is devoted to the
description of the image analysis thesaurus, which:
1) allows systematization of poorly structured and
changing terminology in the domain of image
processing, analysis and understanding; 2) provides
automation of an information retrieval in knowledge
bases for image processing, analysis and
understanding; 3) is a stand-alone reference book,
which helps to navigate in the subject domain.
At present, universal systems designed for image
processing, analysis, and understanding, which are
not related to a specific subject domain, attract a lot
of interest. A knowledge base is the most important
component of such systems. It contains knowledge
on the image processing, on the classes of scenes
analysed, and on the available computational
methods (Bertino et al., 2001). For efficient
information retrieval in the knowledge base it is
necessary to have a tool for semantic interpretation
and matching of textual object descriptions and user
queries. In practice a domain thesaurus can be used
to solve the problem. The relationships between
terms fixed in the thesaurus help to specify and
extend the user query for more successful
information retrieval.
For several recent years the authors have been
developing the knowledge base for the "Black
Square. Versions 1.0, 1.1, 1.2 software system for
the automation of scientific research in image
processing, analysis, recognition, and
understanding" (KBBS 1.0) (Gurevich et al., 1999;
2006).
KBBS 1.0 is aimed at the support and
automation of solving problems of image analysis,
estimation, understanding, and recognition. The
automation depends extremely on solving the
following main problems: 1) automation of image
analysis algorithm retrieval; 2) automation of
algorithm development and combination; 3)
algorithm matching on the basis of its comparative
effectiveness, accuracy, and ability to be
programmed. To solve these problems it is necessary
to use a thesaurus on image processing, analysis and
understanding.
Analysis of the literature testifies that till the
present time no thesauri were developed for the
domain of image processing, analysis, and
recognition. “Image Analysis Thesaurus. Version
1.0” (IAT 1.0) compensates the lack. The thesaurus
presented is being used to solve the following tasks:
194
Trusova Y., Gurevich I., Beloozerov V. and Murashov D. (2007).
LINGUISTIC SUPPORT OF THE KNOWLEDGE BASE FOR IMAGE ANALYSIS AND UNDERSTANDING SYSTEM.
In Proceedings of the Second International Conference on Computer Vision Theory and Applications, pages 194-199
DOI: 10.5220/0002070801940199
Copyright
c
 SciTePress

 classification of algorithms and tasks of image
processing, analysis, understanding and
recognition;
 generation of the descriptions of algorithms and
tasks of image processing, analysis,
understanding and recognition;
 automation of information retrieval;
 classification and retrieval of bibliographic and
reference data.
One of the IAT 1.0 distinctive features is that it
can be used not only as a part of KBBS 1.0, but also
as a separate linguistic resource. IAT 1.0. is a
bilingual and contains terms and their definitions in
two languages (Russian and English).
The IAT 1.0 was applied for automation of early
diagnosis of hematological diseases on the base of
cytological specimens. The application confirmed its
efficiency. Its details will be described in future.
2 THE USE OF IAT 1.0
In general, a thesaurus is a controlled vocabulary of
terms and relationships between them. The thesaurus
structure, its lexical content and program
implementation depend on subject domain
specificity and tasks to be solved (Aitchison et al.,
2002).
IAT 1.0 can be used as a stand-alone reference
book on image processing, analysis and recognition.
It contains definitions of terms and references. IAT
1.0 can be recommended to both professional and
non-professional users. In particular, it will help
those users who are not specialists in the subject
domain to use efficiently KBBS 1.0.
The basic version of IAT 1.0 contains 1538
terms, including 230 terms in "Image" section, 634
terms in "Image Processing" section, 464 terms in
"Image Analysis" section, and 210 terms in "Pattern
Recognition" section. The maximum number of
hierarchy levels is 6.
Below we consider the main functional
characteristics of the IAT 1.0 in the framework of its
use in KBBS 1.0.
2.1 Descriptions of Algorithms
Textual description of an algorithm in KBBS 1.0
consists of a name of a task (goal), a name of an
algorithm, description of input and output data,
context and references. For that terms of the
following functional categories are included in IAT
1.0:
a) "Objects", which includes:
 names of image types (e.g., aspect image, range
image, 2D image, quantized image, etc.);
 names of image elements (e.g., contour, region,
pixel, etc);
b) "Tasks", which includes:
 names of classes of image processing tasks
(e.g., image enhancement, image restoration,
image quantization, etc.);
 names of classes of image analysis tasks (e.g.,
image segmentation, texture analysis, etc.);
 names of classes of pattern recognition
problems, including names of image
recognition tasks (e.g., feature selection, error
estimation, etc.);
c) "Instruments", which includes:
 names of classes of image processing
instruments (methods, algorithms, techniques,
operations, functions, operators,
transformations) (e.g., median filtering,
Hough transform, etc.);
 names of classes of image analysis instruments
(methods) (e.g., contour-based shape
descriptor, region growing method, etc.);
 names of classes of pattern recognition
methods, including names of classes of image
recognition techniques (e.g., maximum
likelihood decision rule, cluster assignment
function, etc.);
d) "Properties", which includes:
 names of instrument properties (e.g., hexagonal
sampling grid, structuring element,
convolution kernel, etc.);
 names of image description elements (e.g.,
brightness, color model, contrast difference,
etc.).
The example of algorithm description is as
follows.
1. Task name: median filtering.
2. Task goal: noise removing.
3. Input data: gray-level image (image depth: 8
bpp; image width: 1024 pixels; image height: – 1024
pixels).
4. Result: gray-level image (image depth: 8 bpp;
image width: 1024 pixels; image height: – 1024
pixels).
5. Operator name: mediana.
Each element of the description is, in turn, an
object characterized by a set of properties. The latter
for such objects can be described by IAT 1.0
descriptors.
LINGUISTIC SUPPORT OF THE KNOWLEDGE BASE FOR IMAGE ANALYSIS AND UNDERSTANDING SYSTEM
195

2.2 Classifications of Algorithms and
Tasks
For automation of image processing, analysis and
recognition the uniform descriptions of standard and
solved tasks were included in the KBBS 1.0.
General classification of tasks of image
processing, analysis and recognition is based on
representation of tasks in the form of a sequence of
operations. In this sequence each operation
corresponds to a task of image processing, analysis
and recognition.
Task classification in the KBBS 1.0 is developed
on the basis of the IAT 1.0. The classification is
based on the functional hierarchical classification of
algorithms for basic operations of image processing,
analysis and recognition.
The following lists are examples of hierarchical
classification of thesaurus terms related to image
processing operations (a) and image processing tasks
(b):
a) image processing operation
geometric image processing operation
linear image processing operation
mathematics-based image processing operation
arithmetic-based image processing operation
image addition
image blending
image division
image multiplication
image subtraction
morphology-based image processing operation
neighborhood image processing operation
non-linear image processing operation
point image processing operation
smoothing image processing operation
...
b) image processing task
image compression
image enhancement
contrast enhancement
histogram equalization
...
edge enhancement
image sharpening
image smoothing
noise suppression
image preprocessing
image restoration
...
2.3 Planning and Control of Problem
Solving
The main objective of the KBBS 1.0 is the support
of planning and control of problem solving. To this
end, the logical and pragmatic relationships between
terms representing task descriptions and solution
techniques should be defined and included into the
thesaurus.
According to the specificity of the domain IAT
1.0 contains the following basic relationships
between descriptors:
 image type – image description element (e.g.,
video-image - aspect ratio);
 process – applied instrument (e.g., edge
detection – Hueckel edge operator);
 image transformation – result (e.g.,
thresholding - binary image);
 applied instrument – instrument characteristic
(e.g., morphologic dilation operator -
structuring element);
 applied instrument – result (e.g., edge detector
– edge map);
 image type – image acquisition technique;
 image type – image transformation.
2.4 Applied Terminology of IAT 1.0
The IAT 1.0 was experimentally tested on the
problems connected with automation of cytological
image analysis. The applied part of the thesaurus
includes the following hematological terms:
 names of blood cells classes;
 names of cells parts and organs;
 names of cells morphological characteristics;
 morphological characteristics values;
 names of physiological processes in blood;
 diagnostic terms.
As a source of hematological terminology we
used atlases of blood cells and tumors of lymphatic
system (Vorob’ev, 2001). The number of term
records is more than 350.
The relationships between hematological
descriptors are defined by standard for thesauri
relationships (ISO-5964, 1985) – hierarchical
generic and whole-part relationships. Other specific
relations between terms are defined by associative
relationships (e.g., relations between names of
morphological characteristics and characteristic
values, relations between characteristics and names
of blood cells classes, and relations between blood
cells classes and diagnostic terms).
3 PROGRAM
IMPLEMENTATION
The IAT 1.0 was implemented in Visual FoxPro 7.0.
The IAT 1.0 program module requires the following
resources:
 Intel Pentium 200 processor and higher;
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 64Мb main memory;
 50Mb for programs;
 operating system - Windows 98/ME/2000/XP.
Detailed description of the IAT 1.0 module is
presented in (Beloozerov et al., 2003).
The thesaurus module provides the following
functions:
 visualizating and editing of the hierarchical
structure of terms;
 adding and editing of terms, descriptors and
records;
 adding and editing of relationships between
terms;
 context searching in the database of a thesaurus.
The IAT 1.0 module consists of the database of a
thesaurus, software tools for database control, a user
interface, a database interface, a LAN interface, and
an Internet interface.
The database of the IAT 1.0 contains descriptor
records and the following main tables:
 the table of descriptors;
 the table of terms;
 the table of definitions;
 the table of relations between terms;
 the table of the types of relations;
 the table of languages;
 the table of user interfaces,
and index files.
The user interface is employed to present a
system of definitions, to visualize the hierarchical
structure of terms and other types of relations
between the terms, to add and edit the records of a
thesaurus, to formulate queries, and to represent the
search results.
The user interface consists of graphic forms and
user menus:
 window menu;
 "Thesaurus display" form (Figure1);
 menu for editing thesaurus structure;
 "Adding and editing of terms" form (Figure 2);
 "Adding and editing of descriptor relationships"
form;
 "Search" form.
4 CONCLUSIONS
At present we are developing the Internet reference-
providing information resource on image processing,
analysis and recognition based on the presented
thesaurus on image analysis.
The Internet is the main and highly available
source of information. So, the data reflecting
achievements and problems in the domain of image
processing, analysis and understanding should be
presented on the Internet.
The available tools for information
representation and retrieval on the Internet do not
provide the effective exploitation of Internet
resources for automated image processing and
analysis. The reasons are insufficient volume of
data, insufficient data systematization and
disadvantages of retrieval tools searching for the
formal coincidence of terms in user query and
information source, and invalidity of data.
For successful information retrieval it is
necessary to have an ontology of the domain of
interest. The practice of development of information
retrieval systems shows that domain ontology can be
adequately represented with the help of information
retrieval thesaurus, where objects are represented by
descriptors and semantic relations of objects are
represented by the formal relations between
descriptors reflecting semantic content of notion,
logical and pragmatic relations between notions.
The Internet resource on image processing,
analysis and recognition will contain:
1) a reference book in the field of image
processing, analysis, and recognition in the
form of a thesaurus;
2) a bibliographic database of descriptions of
papers and monographs, and web links to
the electronic publications in the given
domain;
3) tools for relevant information retrieval on
the Internet;
4) a catalogue of Internet resources on image
processing, analysis, and recognition
including (a) web links to electronic
libraries, (b) web links to bibliographic
databases, (c) a list of the websites of
institutions, scientific centres, laboratories
and IT companies involved in research and
development in the field of image
processing and analysis, (d) a list of the
websites of publishing houses, and (e) a
regularly updating list of relevant
conferences with their websites.
The Internet reference-providing information
resource on image processing, analysis and
recognition will provide integration of existing
information sources and will support intelligent
information retrieval in the domain of image
processing, analysis, recognition and understanding.
LINGUISTIC SUPPORT OF THE KNOWLEDGE BASE FOR IMAGE ANALYSIS AND UNDERSTANDING SYSTEM
197

Figure 1: "Thesaurus display" form.
Figure 2: “Adding and editing of terms” form.
ACKNOWLEDGEMENTS
This work was supported by the Russian Foundation
for Basic Research (project nos. 05-07-08000, 06-
01-81009, and 06-07-89203) and by the project of
the Program of the Presidium of the Russian
Academy of Sciences “Fundamental Problems of
Computer Science and Information Technologies”.
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