=Paper=
{{Paper
|id=Vol-184/paper-1
|storemode=property
|title=An Ontology Framework For Knowledge-Assisted Semantic Video Analysis and Annotation
|pdfUrl=https://ceur-ws.org/Vol-184/semAnnot04-01.pdf
|volume=Vol-184
|dblpUrl=https://dblp.org/rec/conf/semweb/DasiopoulouPMKS04
}}
==An Ontology Framework For Knowledge-Assisted Semantic Video Analysis and Annotation==
https://ceur-ws.org/Vol-184/semAnnot04-01.pdf
An Ontology Framework For
Knowledge-Assisted Semantic Video Analysis
and Annotation
S. Dasiopoulou1,2 , V. K. Papastathis2 , V. Mezaris1,2 , I. Kompatsiaris2 and
M. G. Strintzis1,2
1
Information Processing Laboratory, Electrical and Computer Engineering
Department, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
2
Informatics and Telematics Institute (ITI)/ Centre for Research and Technology
Hellas (CERTH), 1st Km Thermi-Panorama Rd, Thessaloniki 57001, Greece
email: strintzi@iti.gr
Abstract. An approach for knowledge assisted semantic analysis and
annotation of video content, based on an ontology infrastructure is pre-
sented. Semantic concepts in the context of the examined domain are
defined in an ontology, enriched with qualitative attributes of the se-
mantic objects (e.g. color homogeneity), multimedia processing methods
(color clustering, respectively), and numerical data or low-level features
generated via training (e.g. color models, also defined in the ontology).
Semantic Web technologies are used for knowledge representation in
RDF/RDFS language. Rules in F-logic are defined to describe how tools
for multimedia analysis should be applied according to different object
attributes and low-level features, aiming at the detection of video objects
corresponding to the semantic concepts defined in the ontology. This sup-
ports flexible and managed execution of various application and domain
independent multimedia analysis tasks. This ontology-based approach
provides the means of generating semantic metadata and as a conse-
quence Semantic Web services and applications have a greater chance of
discovering and exploiting the information and knowledge in multimedia
data. The proposed approach is demonstrated in the Formula One and
Football domains and shows promising results.
1 Introduction
As a result of recent progress in hardware and telecommunication technologies,
multimedia has become a major source of content on the World Wide Web, used
in a wide range of applications in areas such as content production and dis-
tribution, telemedicine, digital libraries, distance learning, tourism, distributed
CAD/CAM, GIS, etc. The usefulness of all these applications is largely deter-
mined by their accessibility and portability and as such, multimedia data sets
This work was supported by the European Commission under contracts FP6-001765
aceMedia and FP6-507482 KnowledgeWeb.
1
present a great challenge in terms of storing, querying, indexing and retrieval. In
addition, the rapid increase of the available amount of multimedia information
has revealed an urgent need for developing intelligent methods for understand-
ing and managing the conveyed information. To face such challenges develop-
ing faster hardware or more sophisticated algorithms has become insufficient.
Rather, a deeper understanding of the information at the semantic level is re-
quired [1]. This results in a growing demand for efficient methods for extracting
semantic information from such content, since this is the key enabling factor for
the management and exploitation of multimedia content.
Although new multimedia standards, such as MPEG-4 and MPEG-7 [2], pro-
vide the needed functionalities in order to manipulate and transmit objects and
metadata, their extraction, and that most importantly at a semantic level, is
out of the scope of the standards and is left to the content developer. Extraction
of features and object recognition are important phases in developing general
purpose multimedia database management systems [3]. Significant results have
been reported in the literature for the last two decades, with successful imple-
mentation of several prototypes [4]. However, the lack of precise models and
formats for object and system representation and the high complexity of multi-
media processing algorithms make the development of fully automatic semantic
multimedia analysis and management systems a challenging task.
This is due to the difficulty, often mentioned as the semantic gap, in captur-
ing concepts mapped into a set of image and/or spatiotemporal features that
can be automatically extracted from video data without human intervention
[5]. The use of domain knowledge is probably the only way by which higher
level semantics can be incorporated into techniques that capture the semantics
through automatic parsing. Such techniques are turning to knowledge manage-
ment approaches, including Semantic Web technologies to solve this problem [6].
A priori knowledge representation models are used as a knowledge base that as-
sists semantic-based classification and clustering [7, 8]. In [9] and [10] automatic
associations between media content and formal conceptualizations are performed
based on the similarity of visual features extracted from a set of pre-annotated
media objects and the examined media objects. In [11], semantic entities, in the
context of the MPEG-7 standard, are used for knowledge-assisted video analy-
sis and object detection, thus allowing for semantic level indexing. In [12], the
problem of bridging the gap between low-level representation and high-level se-
mantics is formulated as a probabilistic pattern recognition problem. In [13], an
object ontology, coupled with a relevance feedback mechanism, is introduced to
facilitate the mapping of low-level to high-level features and allow the definition
of relationships between pieces of multimedia information.
In this paper, an approach for knowledge assisted semantic content analysis
and annotation, based on a multimedia ontology infrastructure, is presented.
Content-based analysis of multimedia requires methods which will automati-
cally segment video sequences and key frames into image areas corresponding to
salient objects, track these objects in time, and provide a flexible framework for
object recognition, indexing, retrieval and for further analysis of their relative
2
Domain Knowledge Base
Inference
Engine Multimedia
Multimedia
Content
RDFS Content
ontology Main
Processing
Module
Algorithm
Repository Semantic
F-logic Rules
Multimedia
Description
Fig. 1. Overall system architecture.
motion and interactions. This problem can be viewed as relating symbolic terms
to visual information by utilizing syntactic and semantic structure in a manner
related to approaches in speech and language processing [14]. In the proposed
approach, semantic and low-level attributes of the objects to be detected in com-
bination with appropriately defined rules determine the set of algorithms and
parameters required for the objects detection. Semantic concepts within the con-
text of the examined domain are defined in an ontology, enriched with qualitative
attributes of the semantic objects, multimedia processing methods, and numeri-
cal data or low-level features generated via training. Semantic Web technologies
are used for knowledge representation in RDF/RDFS language. Processing may
then be performed by using the necessary processing tools and by relating high-
level symbolic representations to extracted features in the signal (image and
temporal feature) domain. F-logic rules are defined to describe how tools for
multimedia analysis should be applied according to different object attributes
and low-level features, aiming at the detection of video objects corresponding
to the semantic concepts defined in the ontology. The proposed approach, by
exploiting the domain knowledge modelled in the ontology, enables the recogni-
tion of the underlying semantics of the examined video, providing a first level
semantic annotation. The general system architecture is shown in Fig. 1
Following this approach, the multimedia analysis and annotation process
largely depends on the knowledge base of the system and as a result the method
can easily be applied to different domains provided that the knowledge base is
enriched with the respective domain ontology. Extending the knowledge base
with spatial and temporal objects interrelations would be an important step
towards the detection of semantically important events for the particular domain,
achieving thus a finer, high-level semantic annotation. In addition, the ontology-
based approach also ensures that semantic web services and applications have a
greater chance of discovering and exploiting the information and knowledge in
multimedia data.
The remainder of the paper is organized as follows: section 2 a detailed de-
scription of the ontology and rules developed is given, while in section 3, its
application to the Formula One domain is described. Experimental results are
presented in section 4. Finally, conclusions are drawn in section 5.
3
2 Multimedia Analysis Ontology Development and Rule
Construction
In order to realize the knowledge-assisted multimedia content semantic analysis
and annotation technique explained in the previous section, an analysis and
a domain ontology are constructed. The multimedia analysis ontology is used
to support the detection process of the corresponding domain specific objects.
Knowledge about the domain under discourse is also represented in the form
of an ontology, namely the domain specific ontology. The domain-independent,
primitive classes comprising the analysis ontology serve as attachment points
allowing the integration of the two ontologies. Practically, each domain ontology
comprises a specific instantiation of the multimedia analysis ontology providing
the corresponding color models, restrictions e.t.c as will be demonstrated in more
detail in section 3.
Object detection in general considers the exploitation of objects character-
istic features in order to apply the most appropriate detection steps for the
analysis process in the form of algorithms and numerical data generated off-line
by training (e.g. color models). Consequently, the development of the proposed
analysis ontology deals with the following concepts (RDFS classes) and their
corresponding properties, as illustrated in Fig. 2:
– Class Object: the superclass of all video objects to be detected through
the analysis process. Each object instance is related to appropriate feature
instances by the hasFeature property and to one or more other objects
through a set of appropriately defined spatial properties.
– Class Feature: the superclass of multimedia low-level features associated
with each object.
– Class Feature Parameter which denotes the actual qualitative descriptions
of each corresponding feature. It is subclassed according to the defined fea-
tures, i.e. to Connectivity Feature Parameter, Homogeneity Feature
Parameter e.t.c.
– Class Limit: it is subclassed to Minimum and Maximum and allows the
definition of value restrictions to the various feature parameters.
– The Color Model and Color Component classes are used for the rep-
resentation of the color information, encoded in the form of the Y, Cb, Cr
components of the MPEG color space.
– Class Distribution and Distribution Parameter represent information
regarding the defined Feature Parameter models.
– Class Motion Norm: used to represent information regarding the object
motion.
– Class Algorithm: the superclass of the available processing algorithms (A1 ,
A2 ,. . . ,An ) to be used during the analysis procedure. This class is linked to
the FeatureParameter class through the usesFeatureParameter property
in order to represent the potential argument list for each algorithm.
– Class Detection: used to model the detection process, which in our frame-
work consists of two stages. The CandidateRegionSelection involves find-
ing a set of regions which are potential matches for the object to be detected,
4
on
Dependency Detection
hasDetection
has Object
Dependency hasDetection
isA isA Part
Partial Total
Dependency Dependency Detection Part
isA
Object_2 isA
Texture has
Candidate Region Detection
isA hasFeature isA Selection isA Step
Size Object_1
Final Region
hasFeature Selection
Parameter uses Feature
Feature Feature Parameter
isA Parameter Algorithm
Connectivity isA
isA
isA Connectivity
isA isA Four Connectivity
Feature Parameter
Homogeneity has Texture Component Labelling
Earth's Mover
Limit Feature Parameter
isA Distance- EMD
isA
Limit Homogeneity K-means
Feature Parameter
isA isA
Maximum hasColorComponent
Minimum
Motion Norm Color Model Y
Color
Standard isA Cb
Component
Deviation Distribution has
Distribution Cr
hasDistribution Distribution
Parameter
Mean Parameter isA
Value isA
Gaussian
has ParameterValue
"Integer"
Fig. 2. Multimedia analysis ontology.
while FinalRegionSelection leads to the selection of only one region that
best matches the criteria predefined for this object (e.g. size specifications).
– Class Dependency: this concept addresses the possibility that the detec-
tion of one object may depend on the detection of another, due to possible
spatial or temporal interrelations between the two objects. For example in
the Formula One domain, the detection of the car could be assisted and
improved if the more dominant and characteristic region of road is detected
first. In order to differentiate between the case where the detection of object
O1 requires the detection of the candidate regions of object O2 and the case
where the entire final region of object O2 is required, PartialDependency
and TotalDependency are introduced.
As mentioned before, the choice of algorithms employed for the detection
of each object is directly dependent on its available characteristic features. This
association is determined by a set of properly defined rules represented in F-logic.
F-logic is a language that enables both ontology representation and reasoning
about concepts, relations and instances [15, 16].
The rules required for the presented approach are: rules to define the mapping
between algorithms and features (which implicitly define the object detection
steps), rules to determine algorithms input parameters, if any, and rules to deal
5
with object interdependencies as explained above. The rules defined for each
category have the following form:
– “IF an object O has features F1 F2 . . . Fn as part of its qualitative de-
scription THEN algorithm A1 is a step for the detection of O.”
– “IF an object O has feature F AND O has algorithm A as detection step
AND A uses feature F THEN A has as input the parameter values of F .”
– “IF an object O1 has partial dependency on object O2 AND object O2
has as CandidateRegionSelection part the set S = {A1 , A2 , . . . , Am }
THEN execute the set of algorithms included in S before proceeding with
the detection of O1 .”
– IF an object O1 is totally dependent on object O2 THEN execute all detec-
tion steps for O2 before proceeding with the execution of O1 detection.”
In order for the described multimedia analysis ontology to be applied, a
domain specific ontology is needed. This ontology provides the vocabulary and
background knowledge of the domain i.e. the semantically significant concepts
and the properties among them. In the context of video understanding it maps
to the important objects, their qualitative and quantitative attributes and their
interrelations.
3 Domain Knowledge Ontology
As previously mentioned, for the demonstration of the proposed approach the
Formula One and Football domains were used. The detection of semantically
significant objects, such as the road area and the cars in racing video for example,
is an important step towards understanding and extracting the semantics of a
temporal segment of the video by efficiently modelling the events captured in
it. The set of features associated with each object comprises their definitions in
terms of low-level features as used in the context of video analysis. The selection
of the attributes to be included is based on their ability to act as distinctive
features for the analysis to follow, i.e. the differences in their definitions indicate
the different processing methods that should be employed for their identification.
As a consequence, the definitions used for the Formula One domain are:
– Car: a motion homogeneous (i.e. comprising elementary parts characterized
by similar motion), fully connected region whose motion norm must be above
a minimum value and whose size can not exceed a predefined maximum
value.
– Road: a color homogeneous, fully connected region, whose size has to exceed
a predefined minimum value and additionally to be the largest such region
in the video.
– Grass: a color homogeneous, partly connected region with the requirement
that each of its components has a minimum predefined size.
– Sand: a color homogeneous, partly connected region with the requirement
that each of its components has a size exceeding a predefined minimum.
6
In a similar fashion, the corresponding definitions for the Football domain
include the concepts Player, Field and Spectators and their respective visual
descriptions. As can be seen, the developed domain ontologies focus mainly on
the representation of the object attributes and positional relations and in the
current version does not include event definitions. For the same object, multiple
instances of the Color Model class are supported, since the use of more than
one color models for a single object may be advantageous in some cases.
3.1 Compressed-domain Video Processing and Rules
The proposed knowledge-based approach is applied to MPEG-2 compressed
streams. The information used by the proposed algorithms is extracted from
MPEG sequences during the decoding process. Specifically, the extracted color
information is restricted to the DC coefficients of the macroblocks of I-frames,
corresponding to the Y, Cb and Cr components of the MPEG color space. Ad-
ditionally, motion vectors are extracted for the P-frames and are used for gen-
erating motion information for the I-frames via interpolation. P-frame motion
vectors are also necessary for the temporal tracking in P-frames, of the objects
detected in the I-frames [17].
The procedure for detecting the desired objects starts by performing a set of
initial clusterings, using up to eight dominant colors in each frame to initialize a
K-means algorithm. ¿From the resulting mask, which contains a number of non-
connected color-homogeneous regions, the non-connected semantic objects can
be identified by color-model based selection. The application of a four connec-
tivity component labelling algorithm results in a new mask featuring connected
color-homogenous components. The color-model-based selection of an area cor-
responding to a color-homogeneous semantic object is performed using a suitable
mask and the Earth Movers Distance (EMD). EMD computes the distance be-
tween two distributions represented as signatures and is defined as the minimum
amount of work needed to change one signature into the other. Additional re-
quirements as imposed by the models represented in the ontology, are checked to
lead to the desired object detection. For motion-homogeneous objects a similar
process is followed. At first, a mask containing motion-homogeneous regions is
generated. Subsequently, the model- based selection depends on the information
contained in the ontology (e.g. size restrictions, motion requirements).
The construction of the domain specific rules derives directly from the afore-
mentioned video processing methodology. For example, since color clustering is
the first step for the detection of any of the three objects, a rule of the first
category without any feature matching condition is used to add the k-means
algorithm as the first detection step to all objects. A set of different algorithms
could have been used as long as the respective instantiations are defined.
4 Experimental results
The proposed approach was tested in two different domains: the Formula One
and the Football domain. In both cases, the exploitation of the knowledge con-
7
tained in the respective system ontology and the associated rules resulted to
the application of the appropriate analysis algorithms using suitable parameter
values, for the detection of the domain specific objects. For ontology creation the
OntoEdit ontology engineering environment [18] was used, having F-logic as the
output language. A variety of MPEG-2 videos of 720 × 576 pixels were used for
testing and evaluation of the knowledge assisted semantic annotation system.
For the Formula One domain our approach was tested on a one-hour video.
As was discussed in section 3, four objects were defined for this domain. For
those objects whose homogeneity attribute is described in the ontology by the
Color Homogeneity class, the corresponding color models were extracted from
a training set of approximately 5 minutes of manually annotated Formula One
video. Since we assume the model to be a Gaussian distribution for each one of
the three components of the color space, the color models were calculated from
the annotated regions of the training set accordingly. Results for the Formula
One domain are presented both in terms of sample segmentation masks showing
the different objects detected in the corresponding frames (Fig. 3) as well as
numerical evaluation of the results over a ten-minute segment of the test set
(Table. 1). For the Football domain, the proposed semantic analysis and anno-
tation framework was tested on a half-hour video, following a procedure similar
to the one illustrated for the Formula One domain. Segmentation masks for this
domain are shown in Fig. 4, while numerical evaluation of the results over a
ten-minute segment of the test set for this domain are given in Table. 1.
For the numerical evaluation, the semantic objects appearing on each I-frame
were manually annotated and compared with the results produced by the pro-
posed system. It is important to note that the regions depicted in the generated
segmentation masks correspond to semantic concepts and this mapping is de-
fined according to the domain specific knowledge (i.e. object models) provided
in the ontology.
5 Conclusions
In this paper we have presented an ontology-based approach for knowledge as-
sisted domain-specific semantic video analysis. Knowledge involves qualitative
object attributes, quantitative low-level features generated by training as well
as multimedia processing methods. The proposed approach aims at formulating
a domain specific analysis model with the additional information provided by
rules, appropriately defined to address the inherent algorithmic issues.
Future work includes the enhancement of the domain ontology with more
complex model representations, including spatial and temporal relationships,
and the definition of semantically important events in the domain of discourse.
Further exploration of low-level multimedia features (e.g. use of the MPEG-7
standardized descriptors) is expected to lead to more accurate and thus efficient
representations of semantic content. The above mentioned enhancements will
allow more meaningful reasoning, thus improving the efficiency of multimedia
content understanding. Another possibility under consideration is the use of a
8
Fig. 3. Results of road, car, grass and sand detection for Formula One video. Mac-
roblocks identified as belonging to no one of these four classes are shown in white.
Fig. 4. Results of field, player, and spectators detection for Football video. Macroblocks
identified as belonging to no one of these three classes are shown in white.
more expressive language, e.g. OWL, in order to capture a more realistic model
of the specific domain semantics.
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