Visual-CBIR: Platform for Storage and Effective Manipulation
of a Database Images
Kaouther Zekri
1
, Amel Grissa Touzi
2
and Noureddine Ellouze
1
1
ENIT, Tunis El Manar University, SITI, BP 37, Le Belvédère, 1002, Tunis El Manar, Tunis, Tunisia
2
ENIT, FST, Tunis El Manar University, LIPAH, BP 37, Le Belvédère, 1002, Tunis El Manar, Tunis, Tunisia
Keywords: Images Manipulation, Content-based Image Retrieval, Oracle DBMS, Search Queries, Signature, Similarity
Measure, Semantic Description.
Abstract: Today, image retrieval system has become a vital necessity for computing users. Different search systems
are increasingly invading the computing software markets, such as QBIC, Photobook and BlobWord. The
only negative point these systems have in common is their lack of semantics in query processing, low
interactivity and the irrelevance of search results. To overcome these limitations, we propose a more
efficient alternative system: a system for image retrieval. This new system provides an intelligent search by
content, by keyword, by index, etc. To confirm our approach, we have defined a combination with Oracle
DBMS that would lead to 1) an advanced modeling of image type using a signature that describes the
physical and semantic content of images, 2) the modeling of different types of search by creating stored
procedures in PL/SQL language and 3) simple storage and handling of images in database through an
intuitive interface. We prove that this system can be used in a distributed environment.
1 INTRODUCTION
Facing the speedy development of massive
computing information through the web, together
with technology progress, the issue related to data
management became more acute and important in
the context of information analysis and data
visualization. In this work, we focus on image data
that have experienced an evolution in terms of
storage, manipulation and content processing.
Any form of imagery that does not reflect a
specific field as content (science, geography,
physics, literature…) would hardly be classified
thus it would seem without any interest and would
end up in as a random result on a search list. This
kind of search results’ inexactitude proved the
necessity to develop Content-Based Image
Retrieval (CBIR) systems.
Since 1995, several search systems have been
commercialized, such as the systems presented by
Pecenovic (Pecenovic et al., 1998): IBM QBIC,
Photobook MIT Media Lab (Massachusetts
Institute of Technology), BlobWord of the
University of Berkeley in California, Virage and
Cortina. Despite their efficiency, these search
systems suffer from several limitations such as the
limited storage for database used relatively to fit
with the size of the current image database which
involves a different problematic with a very strong
constraints in time computing and the results
relevance. In 2005, Landré (Landré, 2005)
proposed a new architecture of CBIR based on the
hierarchical construction about increasing size
multiresolution signatures, the automatic grouping
of images in visually similar families of images
(database pre-classification), and the design of
images retrieval interface. This system suffers from
several problems such as non- trivial choice
parameters like the blur setting parameter during
the classification phase, which can lead to irrelevant
classification.
This paper aims to explain the system’s
procedure for image retrieval and to propose an
intelligent system for image retrieval (by content, by
keyword, by index, etc.) using Oracle DBMS. To
confirm our approach, we have defined a
combination with Oracle DBMS that would lead to
1) an advanced modeling image type using a
signature that describes the physical and semantic
content of images, 2) the modeling of different types
of search by creating stored procedures in PL/SQL
language and 3) a simple storage and handling of
images in database through an intuitive interface.
81
Zekri K., Grissa Touzi A. and Ellouze N..
Visual-CBIR: Platform for Storage and Effective Manipulation of a Database Images.
DOI: 10.5220/0005518900810090
In Proceedings of 4th International Conference on Data Management Technologies and Applications (DATA-2015), pages 81-90
ISBN: 978-989-758-103-8
Copyright
c
 2015 SCITEPRESS (Science and Technology Publications, Lda.)

The selection of Oracle DBMS is justified by
Oracle Multimedia (formerly Oracle interMedia)
feature that represents an extension to retrieve
multimedia data by their content. This extension
enables Oracle Database to manage, store, and
retrieve images using a new data type that stores the
inherent features of the image and using indexes to
make queries faster.
The second section of this work explains the
architecture of the image retrieval system and the
main query types that were used. In Section 3, we
discuss the reasons of our motivation. Our new
approach will be fully illustrated in Section 4.
Section 5 represents the validation and the
implementation of our system. Section 6 concludes.
2 IMAGE RETRIEVAL SYSTEM
2.1 Classical Architecture of
Content-based Image
Retrieval System
In a content-based retrieval approach, the descriptive
model associated with each image is represented as a
signature (or an image descriptor). This signature is
used to describe the visual content of the image. The
search is based on a comparison between a query
image and images database for classifying images
according to a distance measured between signature
of the query image and the images signature of the
database. Figure1 represents the classical
architecture of CBIR systems.
Figure 1: Classical architecture of a CBIR system.
The architecture of this system is divided into
two phases of treatment:
 The Offline Phase: has a characterization
step in which attributes are automatically
extracted from the images of the database and
stored in vectors. Then, these vectors are
stored in a database of descriptors.
 The Online Phase: aims to extract the
descriptor of the query image given by the
user and compare it with descriptors in
database to select similar images to the query
image.
The system returns the search result in a list of
images ordered according to “the similarity
measure” between their signatures and the signature
of the query image.
2.2 Example of Existing CBIR Systems
The development of several search systems allows
us to facilitate the CBIR task. Among these systems
we can provide: QBIC (Query By Image Content)
(Flickner et al., 1995) of IBM, one of the first search
systems which deals with the image search based on
color, texture, shape and sketch. The BlobWorld
(Carson et al., 1999) of the University of California;
Berkeley; works on homogeneous areas from the
image. It allows recovering from a motion picture,
similar regions in color and texture. Photobook
(Piccard et al., 1996), developed by MIT Media Lab,
offers a powerful search to collect homogeneous
images. The search is possible on three different
criteria: appearance, shape and texture. For recent
systems, we can find img(Rummager) and the
website img(Anaktisi) (Zagoris et al., 2009. ) which
can execute an image search based on query image
with a new set of descriptors which include the
characteristic information of the color and texture in
histogram. Despite their efficiency, these approaches
suffer from several limitations, such as: small image
databases used the lack of semantics in the query
processing, imprecision of results and the lack of
integration of image processing techniques.
The MedFMI-SiR system (Daniel et al., 2011)
represents an open source module that executes
similarity queries over DICOM images integrating
metadata- and content-based image retrieval. This
solution is attached onto the Oracle DBMS,
benefiting from all the capabilities it natively
provides. The main drawback of MedFMI-SiR is
that it uses a specialized method to perform only a
feature extraction of the DICOM type image.
In his approach, Landré proposed a CBIR
system using MySQL DBMS. He represents search
techniques for images using visual navigation in the
database. His system organizes descriptors
according to an increasing size to classify images
into families based on multiresolution signatures.
However, this system suffers from the irrelevant
classification in case of non-trivial choice o f
parameters like the blur setting parameter.
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Moreover, this approach does not support an
integrated search query which joins textual
information and visual content in the retrieval
process.
2.3 Image Retrieval System and DBMS
Faced with a great quantitative evolution of images,
the current DBMS developed their storage and data
management capabilities in order to support the
image databases. As an example of DBMS, we
quote: MySQL and Oracle (Gabillaud, 2009).
In (Landré, 2005), Landré proposed a system of
image retrieval based on the MySQL DBMS. The
architecture of this system consists of a web server
(Apache) that manages dynamic web pages (PHP)
able to access data from the relational database
(MySQL).
In this search system, the construction of the
signature involves the following steps: the wavelet
transformation of images, the extraction of
multiresolution attributes form descriptor vectors,
the organization of attributes in signature vectors
stored in MySQL database and the classification of
signature vectors in families according to distance.
The CBIR task and the building of the image
signature are detached from the MySQL DBMS and
executed in a separate retrieval engine. Only storage
and metadata-based queries are realized using the
MySQL database.
Oracle database is a relational database
management system (RDBMS) that has introduced
multimedia applications and object-oriented
development since version 8. Moving to Oracle
Database 11g, Oracle Multimedia (previously
interMedia) becomes a feature that extends Oracle
Database reliability, availability, and data
management to multimedia content in traditional,
Internet, electronic commerce, and media-rich
applications (Rod et al., 2001).
The Oracle Multimedia architecture defines the
framework through which media content as well as
traditional data are supported in the database (Rod et
al., 2001). This architecture includes three principal
levels: Oracle database server, Oracle database, and
client application. Oracle database server is the
software that manages Oracle databases, and client
applications that interact with the server to add,
update, retrieve or delete data.
Using Oracle multimedia feature, Oracle
Database can store, manage, retrieve, and
manipulate multimedia data, especially the image
data. Oracle supports CBIR task that allows image
retrieval using the Oracle Multimedia OrdImage
object type. This can give the possibility to relieve
image database management and to build new end-
user image retrieval applications. In Oracle
Multimedia, the CBIR has been adopted as a
complementary technique to Text-Based Image
Retrieval (TBIR) (Li et al., 2011).
The type ORDImage provides the ability to
manage all image information as attributes (also
called image metadata). These attributes include:
source for the storage information, contentLength,
height and width of the image, fileFormat,
compressionFormat, contentFormat, and mimeType.
This image metadata is collected and organized in
schema-based XML documents. The metadata are
useful to help search in image datasets by indexing
the metadata for powerful text and thematic media
retrievals using
Oracle Text (Rod et al., 2001).
Oracle Text provides a linguistic analysis on
documents, as well as retrieval text using a variety
of strategies performing keyword searching, context
queries, pattern matching, HTML/XML section
searching, Boolean operations, mixed thematic
queries, and so on.
Oracle Multimedia is a single integrated feature
that enables to integrate the qualities of the CBIR
systems and the TBIR systems into a unique
application (Dimitrovski et al., 2009.). Combining
content-based and text-based image retrieval in an
integrated retrieval engine can lead to a better
accuracy, because one complements the other.
3 MOTIVATION
The approaches proposed in different search systems
such as QBIC, Photobook, Virage and Cortina
represent fairly important work in the Content Based
image Retrieval area. However, these search systems
suffer from several limitations such as the limited
storage for database, the difficulty of handling the
stored images and the image retrieval task with very
strong constraints in time computing, and the
irrelevance of search results.
The approach proposed by Landré represents an
image retrieval system based on multiresolution
signatures stored in MySQL database. For the
signature construction, as we have seen in the
previous section, Landré was forced to go through
several steps to build image signatures in MySQL
DBMS, which does not support the content based
image retrieval task. In this approach, a limitation
comes from the non-trivial choice of parameters
such as blur setting parameter during the
classification phase, which can lead to irrelevant
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data classification. Moreover, this system doesn’t
support the integrated search query which combines
textual-based and content-based image retrieval.
Oracle Multimedia CBIR shows very good
results of image search compared to other active
retrieval systems tested by the proposed
benchmarking system for CBIR in (Harald and Paul,
2010). Moreover, it provides the means necessary to
build an integrated retrieval engine that can execute
optimized search query by combining textual- and
content-based image retrieval.
In section 4, we will propose our new CBIR
approach based on Oracle DBMS.
4 NEW APPROACH
4.1 Principle of Our Approach
In this section, we represent a new approach of
CBIR system based on Oracle database. Our
approach is inspired from a system proposed by
Landré, MedFMI-SiR system, and Oracle functions
to manage the content of the images. To overcome
the difficulties of retrieval and manipulation of
image data in the retrieval systems, such as we have
described in the previous section, we proposed a
new content-based image retrieval system that
allows 1) an advanced modeling of image type using
a signature that describes the physical and semantic
content of images, 2) the modeling of different types
of search by creating stored procedures in PL/SQL
language, 3) a simple storage and handling of
images in database through an intuitive interface,
and 4) an integrated search query combining text-
based and content-based image retrieval.
In the construction phase of feature signatures,
we can distinguish between two types of features:
physical and semantic features (Atnafu Besufekad,
2003).
 Physical Features: or low-level features that
describe the visual content of image. For
example: “how closely its color and shape
match a picture of a specific object”.
 Semantic Features: or high-level features
that describe the semantic content of the
image: they describe the nature of the
relationships that link objects within an image.
We can use traditional text to describe the
semantic significance of the image. Example
of a query type for semantic content: “images
of tumor in the right lobe of the brain”.
In our system, the user disposes of a sample
image to make his query. The search queries relate
to color, texture, shape, location of these criteria in
the image, and a combination of these different
search modes. The system allows you to assign a
weight to give more or less importance to certain
search criteria, such as shape, texture, color and
location. Thus each criterion is assigned a
coefficient representing the importance attached to
it during the comparison.
For text-based image retrieval task, the system
allows the user to assign a textual description to one
or a set of visually similar images when they are
added to the database. After storing the images, the
system extracts all image
content and a rich set of
metadata attributes. Once the image metadata has
been extracted and stored, the system indexes the
metadata and keywords assigned for each image for
powerful thematic image retrieval based on Oracle
Text. Thus, the database can locate easily the image
data with indexes that can speed up queries based on
metadata and keywords stored with the image.
Moreover, our system can allow users to provide both
textual- and content-based image retrieval to define
the desired result.
The interface of our system provides the ability
to visualize the images in the database in order to
have a clear idea of what we want and to manage
the contents of the database by adding or removing
images.
As has already been explained, we will treat the
architectures proposed by Landré and MedFMI-SiR
system by using Oracle DBMS to develop our
CBIR system. In addition, we will try to extract
physical features (visual content) and semantic
features (textual information) from images in order
to improve and speed up our search by indexing the
signature and the textual information of image.
4.2 Architecture of Our Approach
The architecture of our system is shown in Figure 2.
The system provides an interface for easy
management of image by storing, removing images
and visual allowing navigation in database. Our
approach replaces the construction sequence of
image signatures (feature extraction, descriptor
vectors, and signature vectors) by a single step of
generating signatures using Oracle functionalities.
At the end of each step of retrieval phase, an option
to generate a SQL script for each data manipulation
will be given to visualize the execution validity of
tasks in complete transparency.
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Figure 2: Architecture of our CBIR system.
The content image retrieval process in our
system is based on extracting color, texture, shape,
and where in the image these features are locating
and comparing them. The features are extracted by
1) segmenting the image into regions according to
color, 2) determining the features for each region.
Color and texture information are determined
globally, by unifying the region-based information,
to generate global color and texture histograms. The
region, texture, global color, and shape information are
stored in the signature to represent these attributes for
the entire image
. This generated signature is stored in
the signature-database.
For the online phase, when the user selects a query
image, the system is responsible for registering the
image in a temporary table in the Oracle database to be
able to generate its signature. Then, a comparison will
be made automatically with the database signatures to
return a list of selected images whose content is very
close to the query image. To measure the distance
between signatures in comparison phase, the system
uses the Manhattan distance.
To execute integrated image retrieval, the system
allows posing integrated queries for text-based and
content-based search. After selecting an example
image, the user enters a few keywords that describe the
content of the image he seeks. The textual description
and the generated signature of the example image will
be used to locate and select from the indexed database
the images that share the same visual features and
textual information with the proposed query.
Finally, we can deal with the phase of
interpretation of the result obtained by the textual
description of the semantic content of the selected
images. This last step is based on the keywords
extracted from database for all selected images in
the search phase in order to semantically describe
the content of images.
5 VALIDATION OF
CONTENT-BASED IMAGE
RETRIEVAL BASED ON
ORACLE DBMS
5.1 Implementation of CBIR System
and Database under Oracle
The implementation of a CBIR system in Oracle is
always a difficult task especially with the huge mass
and different types of images stored in database. In
fact, this implementation, hand-signed by the
designer, can make the CBIR like a delicate task that
requires a thorough knowledge of Oracle database
with a remarkable lack of transparency in handling
image data.
Usually, the designer should stick to the
following measures:
 Creating the images database.
 Textual description of the database content.
 Manual selection of search criteria
corresponding to the type of images database.
 Defining PL / SQL scripts for each operation
performed in the database to make image
management and search task automatic and
transparent.
The designer must define a threshold of
similarity measurement between images in database
in order to select the list of closest images to the
image query.
To illustrate the tasks required to implement our
CBIR example, we represent in the following a set
of scripts to implement two tables images and
image_signature, add an image (Figure 3) to the
database and implement the CBIR task. For
example, the following script can be issued to create
images, image_metadata, and image_signature
tables to store the image with its metadata and
signature:
-- images table
CREATE TABLE images (
id INTEGER PRIMARY KEY,
image ORDSYS.ORDImage,
description varchar2(255));
-- image_metadata table
CREATE TABLE image_metadata(id
INTEGER PRIMARY KEY REFERENCES
images(id),
metaORDImage XMLTYPE,
XMLType COLUMN metaORDImage
XMLSCHEMA http://xmlns.oracle.com
/ord/meta/ordimage
ELEMENT "ordImageAttributes"
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-- image_signature table
CREATE TABLE
image_signature (
id INTEGER PRIMARY KEY
REFERENCES images(id,
signature_img
ORDSYS.ORDImageSignature);
where OrdImage is the Oracle’s abstract data type to
store images. If the image content is stored in the
database, it can be handled as another relational data
using SQL. The ORDImageSignature object type
supports content-based image retrieval, or image
matching, where signature_img is an attribute that
stores image signature. metaORDImage is the
column where the ordimage metadata will be stored.
It is bound to the XML schema stored at
http://xmlns.oracle.com/ord/meta/ordimage, and
defined as the XML element ordImageAttributes.
To add an image to Oracle database, we propose
an example of a non-expert user of images database
who wants to insert a "cerebrale.jpg" (Figure 3) file.
This image is situated in a logical folder
"IMG_DOSSIER" created under oracle by the user.
Figure 3: A medical image selected to be added to the
database.
By adding an ORDImage to the database we
intend to the addition of an image and the generation
of its signature in an ORDImageSignature type
attribute. The set of visual attributes is stored in a
BLOB. Before the generation of an image signature,
it must, like the type ORDImage call a builder, called
init(), which is used to initialize the BLOB attribute
to an empty BLOB. Once this realized, we can call
the method generateSignature by giving the
parameter ORDImage attribute to obtain its visual
characteristics.
--initialization and image insertion
INSERT INTO images (id, image)
VALUES(id_val, ORDImage.init());
INSERT INTO image_signature (id,
signature_img) VALUES (id_val,
ORDImageSignature.init());
SELECT i.image, s.signature_img into
obj, obj_sign from images i,
image_signature s where i.id= s.id
and s.id=id for update;
obj.setSource('FILE','IMG_DOSSIER',
'cerebrale.jpg');obj.import(ctx);
UPDATE images set image = obj where
id=1;
--generation of image signature
obj_sign.generateSignature(obj);
UPDATE image_signature p SET
p.signature_img = obj_sign WHERE
id=id_val;
COMMIT;
END;/
To perform the CBIR task, the user needs to
implement the following PL/SQL script:
DECLARE
img_sig ORDSYS.ORDImageSignature;
img_sig_req
ORDSYS.ORDImageSignature;
-- Specify a weight for criteria
commande varchar(200) := 'color=0
texture=1 shape=0 location=0';
eval_score float;
threshold float;
similaire int;
BEGIN
SELECT p.signature_req INTO
img_sig_req FROM image_requete p
WHERE p.id_req = 1 FOR UPDATE;
FOR record_img IN (SELECT id,
signature_img FROM
image_signature
FOR UPDATE) LOOP
-- Comparison between signatures
threshold := 20;
similaire :=
OrdImageSignature.isSimilar
(record_img. signature_img,
img_sig_req, commande,
threshold);
DBMS_OUTPUT.PUT_LINE('
isSimilar'|| similaire);
END LOOP;
COMMIT;
END;/
In the retrieval phase, it is necessary to make the
comparison between the signature of an image query
and all image signatures stored in the database. The
image signature is obtained through an Oracle
analysis of the image with generateSignature()
method of ORDImage type. This signature contains
color information, textures and shapes according to
each area of the image. It also contains information
on the image background. The result of this analysis
is then contained in a type called
ORDImageSignature.
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Seen that the database is a medical images
database, the user must give more importance to the
texture criterion to execute a texture-based retrieval
query. Therefore, the user must assign a weight in
the range 0 to 1 for color, shape, texture, and
location. To specify a high importance to the texture
criterion, the user should select a high weight for
texture. In our application, a high weight represents
a value of 1, a medium weight is a value of 0.5, and
a low weight represents a value of 0.
The operator used for ORDImage object type is:
isSimilar(). This operator compares the signature of a
query image with the signatures of images stored in a
database, and determines whether or not the images
match, based on the weights assigned to the search
criteria and threshold value. The comparison of two
signatures returns a score between 0.0 and 100.0,
where a lower value indicates a closer match. The
choice of threshold is empirical and it is vital to try to
model and control the choice of appropriate threshold
for a specific image type. Currently, we use an
approximate value threshold that does not exceed 20.
The value of the threshold must be comprised
between 0 and 100, which is the range of the
distance. Then only images whose signatures are a
distance (score) of 20 or less from the query
signature will be selected.
To improve the search performance and easily
execute the task of CBIR, we have implemented an
index-based retrieval on the image signature. This
index is of type ORDImageIndex. By defining this
index item we can filter and locate specific
information and then retrieve it as efficiently as
possible. Therefore, we can have an advantage of the
increased performance based on image matching
with image signature indexes on the signatures
database. The following command creates an index
on image_signature table, based on the data in
the signature_img column:
CREATE INDEX indexSign ON
image_signature
(signature_img)
INDEXTYPE IS ORDSYS.ORDIMAGEINDEX
PARAMETERS
('ORDImage_Filter_Tablespace =
<name>,ORDImage_Index_Tablespace =
<name>');
To easily locate the image data based on textual
information, we implemented a full text index based
on Oracle Text. Oracle Text is an extensive full text
indexing technology that uses standard SQL to
index, analyze, and search text and documents stored
in the Oracle database. It can be used to index XML
data. All image metadata is extracted in the form of
XML database objects and returned as a database
XMLSequence type. This XML format is easier to
be used with database features and to be searched
with Oracle text. The following command represents
a query that selects the OrdImage attribute of image
compressionFormat:
SELECT i.image
FROM images i, image_metadata e
WHERE i.id = e.id
AND extractValue(metaordimage,
'/ordImageAttributes/
compressionFormat/text()',
'xmlns="http://xmlns.oracle.com
/ord/meta/ordimage"')= 'JPEG' ;
where extractValue is the function that scans the
XML data and identifies the elements that satisfy the
given query. Image retrieval based on Metadata
queries can be efficiently performed using a b-tree
index which can be created on the extractValue
function result. This index is employed to
dramatically increase the speed of the previous
query:
CREATE INDEX indexmeta
ON images e
(extractValue(metaordimage,
'/ordImageAttributes/
compressionFormat/text()','xmlns="
http://xmlns.oracle.com/ord/meta/
ordimage"'));
For the text retrieval, we used the context index
type of Oracle text indexes. In the following
command, we represent an example of context index
created on the description column that contents
semantic descriptions assigned for each image stored
in database.
CREATE INDEX textindex
on images (description)indextype
is ctxsys.context;
The integrated search allows posing queries
mixing the textual and visual information to achieve
a better accuracy and flexibility regarding query
resulting. By using a combination of different search
queries which we have already executed, we were
able to implement an integrated query which allows
the selection of images whose signatures are the most
similar to the given example image, and have a
semantic description about brain with tumor. This
query selects only the image which characterized by
JPEG compression format:
SELECT i.image FROM images i,
image_signature s, image_metadata m
WHERE i.id = s.id AND i.id=m.id
AND ORDSYS.IMGSimilar
(s.signature_img, signature_query,
Commande, threshold)=1
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AND contains( description,
'brain,tumor') > 0
AND extractValue (metaordimage,
'/ordImageAttributes/
comprssionFormat/text()','xmlns=
"http://xmlns.oracle.com/ord/meta/
ordimage"')= 'JPEG';
Therefore, our system makes possible to integrate
content-based and text-based retrieval for efficient
image search by
executing queries through indexed
searching over triple image similarities, textual
descriptions, and metadata attributes.
5.2 Intelligent-CBIR System
To remedy the lack of transparency and the
implementation sensitivity of Oracle CBIRS, we
propose a new layer that allows efficient
manipulation of images data type and search by
content in full transparency through a simple and
intuitive interface. To implement our tool, we used
Microsoft Windows Seven software environment.
We use the Oracle 11g release2 database. For the
development environment we use NetBeans IDE
7.4.
Intelligent-CBIR system provides designers with
multiple screens. We illustrate some examples as
follows:
Once the user chooses to connect to the database
by clicking on the corresponding visual navigation
button, the system loads the images in the database
to allow the user to have an idea about the database
content (Figure 4).
Using visual navigation, the user can get a
general idea about the database content that will help
him easily manipulate images by adding or deleting
options. Figure 5 shows an example of adding an
image to the database. To add an image to the
database, you simply select an image through the
system interface and validate your choices. SQL
script will be displayed for each handling contents of
the database for further data manipulation
transparency in our system.
For the task of CBIR, the system allows selecting
an image query and selecting search criteria with
their degree of importance according to the
information provided by the system or the user's
choice as shown in Figure 6:
 The Color Criterion: shows the distribution
of colors in the entire image.
 The Texture Criterion: represents the low-
level models and textures in the image.
 The Shape Criterion: represents the forms
that appear in the image. They can be
determined by techniques based on color
segmentation.
 The Location Criterion: shows the positions
of shape, color and texture.
Figure 4: Interactive visual browsing of images databases.
Once the user validates the choice of search
criteria and starts the images retrieval, the system
browse through all images in database and compares
the signature of the image query by those of images
database.
Figure 5: Adding an image to the database.
For each comparison, a value of similarity
measurement is associated with each image: all
images, whose value of similarity measurement is
lower than a threshold (specified by the
administrator), will be selected and displayed as
search results (Figure 6).
5.3 Type of Search Query in Our
System
This solution offers the user the flexibility for
handling the choice of search criteria, using a single
criterion or a combination of criteria, which allows
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multiple types of queries such as: query by color,
query by texture, and query by shape and texture etc.
 Query by Color: search request relates to the
color feature that is used to measure the
similarity between the images based on the
color criterion. This query gives more
importance to color in the comparison phase
between images.
 Query by Texture: search request relates to
the texture feature that represents the visual
patterns having homogeneity properties
which do not result from the presence of a
single intensity or color only. Texture
determination is ideally suited for medical
image retrievals (Lehmann, 2005).
 Query by Shape: search request relates to
the shape feature to measure the similarity
between different shapes contained in the
images.
 Query by Combination of Different Search
Criteria: search query involves a
combination of criteria. Example of this type
of query "homogeneous texture images and
even distribution form."
 Query by Keywords/Metadata: search
query based on a set of keywords which can
describe the semantic content of the image.
The metadata-based query performs an image
retrieval based on the attributes attached to
each image file which can be extracted and
stored in the database.
 Integrated Query: this type of query is used
in order to optimize the search task. We have
achieved a combination of textual search (for
the semantic content of the image) and
content search (for the physical content of the
image) in order to enhance the results and
provide a semantic interpretation of the
image content.
Figure 6 illustrates an example of image
retrieval based on the texture and shape criteria.
Since we are working on a database test containing
medical images, we chose to give more importance
to the texture criterion (Thomas, 2004) and less
importance to the color, shape and location criteria.
After each comparison, the system displays a
measure of similarity (score) for each image, which
is the rate of correspondence between the query
image and the images of our database. Only images
which have a score below a certain threshold will
be selected.
Figure 6: Content-based image retrieval interface.
5.4 Comparison with -- Other
Approaches
Table 1 shows a comparison between the
functionality provided by our system, the Landré
approach and MedFMI-SiR system.
Table 1: Comparative table between our CBIR system and
existing systems.
Functionalities
Landré
approach
MedFMI-
SiR
Our
system
Multi-types of
content search
Search by physical
content
Search by
semantic content
Semantic
interpretation
Manipulation of
images database
Integrated search
queries
In our system, we were able to model the image
data by the feature extraction and semantic
interpretation of the visual content of the image.
We make possible to integrate text- and content-
based retrieval in order to execute an efficient
image search. We could also provide a simple and
transparent handling (compared to other systems) of
data stored in the Oracle database by adding or
deleting images through a simple interface of our
system.
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6 CONCLUSIONS
With the great mass of data stored in image
databases, content-based image retrieval has become
a necessity in order to classify images and extract
useful information from this large amount of data. In
this approach we have developed a simple and
intuitive interface which ensures an advanced
manipulation of images using Oracle database. One
of the advantages of using a DBMS to manipulate
images is to be able to search for images in many
ways, as well as using a centralized manageable
repository.
Through the study of the implementation manner
of the content based image retrieval in Oracle and
taking into consideration the absence of a simple and
intuitive interface that allows user to do an
intelligent and automatic search for images in
database, we decided to create a layer of assistance
to design and implement CBIR system. The result of
this work is a search system that allows visual
navigation, manipulation of the image database and
CBIR. In addition, Oracle is a distributed DBMS
(Özsu and Valduriez, 2011); so we can model our
system directly into a distributed environment.
The CBIR in our approach is based only on the
Oracle provided features. The future proceedings
also involve integration of our own feature
extraction and signature construction methods.
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