ISO/IEC TR 15938-8:2002
(Main)Information technology — Multimedia content description interface — Part 8: Extraction and use of MPEG-7 descriptions
Information technology — Multimedia content description interface — Part 8: Extraction and use of MPEG-7 descriptions
ISO/IEC TR 15938-8:2002 forms an informative part of ISO/IEC 15938 on extraction and use of metadata descriptions for multimedia content. ISO/IEC TR 15938-8:2002 provides two types of information: informative examples that illustrate the instantiation of description tools in creating descriptions conforming to ISO/IEC 15938, and detailed technical information on extracting descriptions automatically from multimedia content and using them in multimedia applications. ISO/IEC TR 15938-8:2002 is a companion for ISO/IEC 15938-3 (Visual) and ISO/IEC 15938-5 (Multimedia Description Schemes), which provide normative definitions of the description tools. Effort has been made in this Technical Report to preserve the subclause numbering of ISO/IEC 15938-3 and ISO/IEC 15938-5 to allow easy mapping of the information on extraction and use with those technical specifications.
Technologies de l'information — Interface de description du contenu multimédia — Partie 8: Extraction et utilisation des descriptions MPEG-7
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ISO/IEC TR 15938-8:2002(E)
3.12.4 Probability models
3.12.4.1 ProbabilityModel DS
Information on extraction and use is not provided.
3.12.4.2 ProbabilityDistribution DS
3.12.4.2.1 ProbabilityDistribution DS examples
The following example shows the use of the ProbabilityDistribution DS for describing a probability
distribution in terms of its statistics such as mean, variance, min, max, mode, median, and moment.
dim="2">
0.25 0.5
0.1 0.9
0.0 0.0
1.0 1.0
0.5 0.5
0.5 0.5
0.2 0.3
3.12.4.3 Discrete distribution description tools
3.12.4.3.1 Discrete distribution description tools examples
The following example illustrates the use of the HistogramProbability DS for describing an eight-
dimensional histogram.
dim="8">
0.125 0.0 0.0 0.25 0.125 0.25 0.25 0.0
The following example illustrates the use of the BinomialDistribution DS for describing a one-
dimensional binomial distribution.
0.5
16
The following example illustrates the use of the BinomialDistribution DS for describing a two-
dimensional hypergeometric distribution.
5 5
8 8
16 16
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The following example illustrates the use of the PoissonDistribution DS for describing a three-
dimensional Poisson distribution.
0.4 0.3 0.75
The following example illustrates the use of the GeometricDistribution DS for describing a one-
dimensional geometric distribution.
0.75
The following example illustrates the use of the DiscreteUniformDistribution DS for describing a
one-dimensional discrete uniform distribution.
0.125
2.0
8.0
3.12.4.4 Continuous distribution description tools
3.12.4.4.1 Continuous distribution description tools examples
The following example illustrates the use of the GaussianDistribution DS for describing a one-
dimensional Gaussian distribution.
0.5
0.25
The following example illustrates the use of the ExponentialDistribution DS for describing a two-
dimensional generalized Gaussian distribution.
0.5 0.35
0.25 0.75
2 2
The following example illustrates the use of the ExponentialDistribution DS for describing a two-
dimensional exponential distribution.
0.5 0.25
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The following example illustrates the use of the GammaDistribution DS for describing a three-
dimensional Gamma distribution.
0.4 0.3 0.75
8 4 16
The following example illustrates the use of the ContinuousUniformDistribution DS for describing a
one-dimensional continuous uniform distribution.
1.0
0.0
The following example illustrates the use of the LognormalDistribution DS for describing a one-
dimensional lognormal distribution.
0.5
0.35
3.12.4.5 Finite state model description tools
3.12.4.5.1 Finite state model description tools examples
The following example shows the use of the StateTransitionModel DS for describing a state-transition
model that has three states. In this example, assume that the observed weather has one of the following
states: "precipitation", "cloudy", or "sunny". Furthermore, the state-transition probabilities indicate the
probability of moving from one state to another. The initial probabilities specify the initial probability of each
state. The state-transition model allows questions to be answered such as, what is the probability that the
weather for eight consecutive days is "sun-sun-sun-rain-rain-sun-cloudy-sun", or given that the system is in a
known state, i.e., "sunny", what is the expected number of consecutive days the system will remain in that
state.
0.5 0.25 0.25
0.4 0.3 0.3 0.2 0.6 0.2 0.1 0.1 0.8
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ISO/IEC TR 15938-8:2002(E)
The following example shows the use of the StateTransitionModel DS for describing a state-transition
model of the events depicted a video of a soccer game. The model describes three states: "Pass", "Shot on
goal" and "Goal score". The StateTransitionModel DS describes the states, the initial state
probabilities, and the transition probabilities between the states.
0.25 0.5 0.25
0.2 0.2 0.6 0.1 0.8 0.1 0.3 0.3 0.4
The following example shows the use of DiscreteHiddenMarkovModelType for describing a discrete
hidden markov model of events in a soccer game, such as "passes" and "goal scores".
0.5 0.5
0.4 0.6 0.3 0.7
2
Team A pass
Team B pass
momentNormalized="1" dim="2">
0.2 0.8
2
Team A goal
Team B goal
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ISO/IEC TR 15938-8:2002(E)
momentNormalized="1" dim="2">
0.4 0.6
The following example shows the use of the ContinuousHiddenMarkovModel DS for describing a
continuous hidden Markov model of audio sound effects. Each continuous hidden Markov model has 5
states and represents a sound effect class. The parameters of the continuous density state model can be
estimated via training, for example, using the Baum-Welch algorithm. After training, the continuous HMM
model consists of a 5x5 state transition matrix, a 5x1 initial state density matrix, and 5 multi-dimensional
Gaussian distributions defined in terms of the mean and variance parameters. Each multi-dimensional
Gaussian distribution has six dimensions corresponding to audio features comprised of 5 channels of
Independent Component Analysis (ICA) data and 1 channel of spectral envelope data.
0.1 0.2 0.1 0.4 0.2
0.2 0.2 0.6 0.0 0.0
0.1 0.2 0.1 0.3 0.3
0.4 0.2 0.1 0.1 0.2
0.2 0.1 0.4 0.2 0.1
0.0 0.2 0.1 0.3 0.4
1 2 3 4 5 6
BeatData
0.5 0.5 0.25 0.3 0.5 0.3
0.25 0.75 0.5 0.45 0.75 0.3
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0.25 0.4 0.25 0.3 0.2 0.1
0.5 0.25 0.5 0.45 0.5 0.2
0.2 0.5 0.35 0.3 0.5 0.5
0.5 0.5 0.5 0.5 0.75 0.5
0.5 0.3 0.25 0.2 0.5 0.6
0.5 0.1 0.5 0.25 0.75 0.4
0.5 0.15 0.25 0.3 0.5 0.35
0.5 0.75 0.5 0.5 0.75 0.35
3.12.4.5.2 FiniteStateModel DS use
State-transition models can be extracted from the events along the temporal dimension of a video sequence.
For example, a temporal sequence of scenes in video can be characterized by a state-transition model that
describes the probabilities of transitions between scenes.
The state-transition models can be matched using the following matching metrics to determine the similarity
of the underlying multimedia content being modeled:
Euclidean distance: calculates the sum of squared differences between transition probabilities.
2
dissimilar score� �A � B �
�� ij ij
ij
Quadratic distance: calculates the sum of weighted quadratic distance between transition probabilities.
�A � B ��A � B �
ij ij kl kl
dissimilar score�
����
��1� abs(i� k)� abc( j� l)
ij k l
Weighted transition frequency: calculates the weighted sum of ratios of transition probabilities.
A
ij
fA � A � A rA �
ij ij ji ij
A
ji
� fA fB � � rA rB �
ij ij ij ij
� � � �
match score � fA � fB � min , � min ,
�� ij ij
� � � �
fB fA rB rA
iij�
ij ij ij ij
� � � �
Euclidean distance of aggregated state transitions: calculates the sum of the squared differences of
aggregated transitions.
2
2
� �
� �
� �
dissimilar score � A � B � � A � B �
��� ij ij��� ij ij
� �
ijj jii
� �
� �
3.12.5 Analytic models
3.12.5.1.1 AnalyticModel DS examples
The example below illustrates the use of the ModelState DS to describe a an analytic model that
represents a state in a finite state model. The description gives three labels for the state and gives
information about the confidence and reliability of the state analytic model.
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ISO/IEC TR 15938-8:2002(E)
3.12.5.2 CollectionModel DS
3.12.5.2.1 CollectionModel DS examples
The following example illustrates the use of the CollectionModel DS for describing a collection model
consisting of a content collection of four images. In this example, the semantic concept is being described by
the collection of images. For example, the collection of images describe the concept of "soccer shots on
goal". In this example, the description gives a confidence of 0.75 and a reliability of 0.5.
function="described">
soccer1.jpg
soccer2.jpg
soccer3.jpg
soccer4.jpg
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The following example illustrates the use of the CollectionModel DS for describing a model of a
collection of two color descriptions. In this example, the semantic concept "Sunsets" has the function of
describing the collection of descriptors.
function="describing">
numOfBitplanesDiscarded="0">
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6
numOfBitplanesDiscarded="0">
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6
The following example illustrates the use of the CollectionModel DS for describing a collection of of four
concepts. The concept collection has a semantic label: "soccer stadium objects", that is, the semantic
concept has the function of describing the collection of concepts. In this example, the confidence is 0.75 and
reliability is 0.5.
function="describing">
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ISO/IEC TR 15938-8:2002(E)
3.12.5.3 DescriptorModel DS
3.12.5.3.1 DescriptorModel DS examples
The following example illustrates the use of the DescriptorModel DS for describing a descriptor
model of the ScalableColor descriptor. The DescriptorModel DS specifies that the descriptor model is
formed from the Coefficients element of the ScalableColor D (defined in ISO/IEC 15938-3). For
example, in the DescriptorModel for ScalableColorType, the value of numOfCoefficients="16" is
constant in the descriptor model.
numOfBitplanesDiscarded="0">
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6
Coeff
The following example illustrates the use of the DescriptorModel DS for describing a descriptor
model of the ContourShape descriptor. The DescriptorModel DS specifies that the descriptor model is
formed from the concatenation of six elements of the ContourShape D (defined in ISO/IEC 15938-3). All
other elements and attributes not mentioned in the field statements are assumed to be constant in the
descriptor models, taking the values specified in the example Descriptors.
3 5
4 7
2
GlobalCurvature
PrototypeCurvature
HighestPeakY
Peak
3.12.5.4 ProbabilityModelClass DS
3.12.5.4.1 ProbabilityModelClass DS examples
The following example illustrates the use of the ProbabilityModelClass DS for describing a probability
model class that characterizes a class of "Nature scene" images by representing the statistics associated
with the color features of the images of that class. For example, the specification below indicates that the
Coefficients element of the ScalableColor D (defined in ISO/IEC 15938-3) for the Nature scene
images has a centroid or mean value of (0.5, 0.5, …) and a variance of (0.25, 0.75, …).
reliability="0.5">
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numOfBitplanesDiscarded="0">
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6
Coeff
confidence="1.0"
dim="16">
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 5
The following example illustrates the use of the ProbabilityModelClass DS for describing a probability
model that characterizes checker patterns using a probability model of EdgeHistogram D (see ISO/IEC
15938-3).
reliability="0.5">
3 5 2 5 . . . 6
BinCounts
confidence="0.75"
dim="80">
5 3 9 4 . . . 5
4.5 5.0 8.0 7.5 . . . 5.2
The following example illustrates the use of the ProbabilityModelClass DS for describing a probability
model that characterizes oval shapes using a probability model of RegionShape D (see ISO/IEC 15938-3).
reliability="0.5">
3 5 2 5 . . . 6
MagnitudeOfART
confidence="1.0"
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ISO/IEC TR 15938-8:2002(E)
dim="35">
4 8 6 9 . . . 5
1.3 2.5 5.0 4.5 . . . 3.2
The following example illustrates the use of the ProbabilityModelClass DS for describing a probability
model that characterizes silhouettes using a probability model of ContourShape D (see ISO/IEC 15938-3).
reliability="0.5">
3 5
4 7
2
GlobalCurvature
PrototypeCurvature
HighestPeakY
Peak
confidence="1.0"
dim="11">
3 9 7 4 6 9 1 2 6 8 9
3.4 5.4 9.1 1.8 2.3 6.5 7.9 1.2 3.4 3.3
1.0
3.12.6 Cluster models
3.12.6.1.1 ClusterModel DS examples
The following example illustrates the use of the ClusterModel DS for describing a cluster model consisting
of a set of examples of two scalable color descriptions. The cluster models have a semantic label: "Nature
scenes". The example describes also the probability model of the ScalableColor D (see ISO/IEC 15938-
3).
numOfBitplanesDiscarded="0">
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6
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numOfBitplanesDiscarded="0">
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6
numOfBitplanesDiscarded="0">
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6
Coeff
confidence="1.0"
dim="16">
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 5
3.12.7 Classification models
Information on extraction and use is not provided.
3.12.7.1 ClusterClassificationModel DS
3.12.7.1.1 ClusterClassificationModel examples
The following example illustrates the use of the ClusterClassificationModel DS for describing a
cluster classification model related to scenes from a soccer game. In this example, the
ClusterClassificationModel is comprised of two ClusterModels. The first ClusterModel
describes a cluster of two images that form the class with label "Soccer shots on goal". The second
ClusterModel describes a cluster of two images that form the class with label "Goal scores".
reliability="0.8" complete="true" redundant="false">
soccer1.jpg
soccer2.jpg
soccer3.jpg
soccer4.jpg
The following example illustrates the use of the ClusterClassificationModel DS for describing a
cluster classification model related to images depicting nature scenes. The
ClusterClassificationModel is comprised of two ClusterModels. The first ClusterModel
describes a collection of two scalable color descriptions that forms the class with label "Sunsets". The
second ClusterModel describes a collection of two scalable color descriptions that forms the class with
label "Nature scenes".
reliability="0.75" complete="true" redundant="false">
numOfBitplanesDiscarded="0">
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6
numOfBitplanesDiscarded="0">
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6
numOfBitplanesDiscarded="0">
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6
numOfBitplanesDiscarded="0">
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6
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ISO/IEC TR 15938-8:2002(E)
3.12.7.2 ProbabilityClassificationModel DS
3.12.7.2.1 ProbabilityClassificationModel DS examples
The following example illustrates the use of the ProbabilityClassificationModel DS for describing a
probability classification model for sunset and nature images. The ProbabilityClassificationModel
DS is comprised of two instances of ProbabilityModelClass DS. The first ProbabilityModelClass
DS instance specifies a class of scalable color descriptors that forms with label "Sunsets". The second
ProbabilityModelClass DS instance specifies a class scalable color descriptors with label "Nature
scenes".
reliability="0.75" complete="true" redundant="false">
numOfBitplanesDiscarded="0">
4 5 6 7 8 9 0 1 2 3 4 5 6 1 2 3
Coeff
confidence="1.0" dim="16">
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
5 6 7 8 9 0 1 2 3 4 5 6 5 2 3 4
numOfBitplanesDiscarded="0">
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6
Coeff
...
TECHNICAL ISO/IEC
REPORT TR
15938-8
First edition
2002-12-15
Information technology — Multimedia
content description interface —
Part 8:
Extraction and use of MPEG-7
descriptions
Technologies de l'information — Interface de description du contenu
multimédia —
Partie 8: Extraction et utilisation des descriptions MPEG-7
Reference number
ISO/IEC TR 15938-8:2002(E)
©
ISO/IEC 2002
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ISO/IEC TR 15938-8:2002(E)
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Published in Switzerland
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ISO/IEC TR 15938-8:2002(E)
Contents
Foreword.vi
Introduction .vii
1 Scope.1
2 Terms and definitions.1
2.1 Conventions.1
2.1.1 Description tools .1
2.1.2 Naming convention .1
2.2 Terminology.2
2.2.1 Schema-related terminology .2
2.2.2 Content-related terminology .2
2.3 Symbols and abbreviated terms.6
2.3.1 Generic .6
2.3.2 Arithmetic operators .6
2.3.3 Logical operators.7
2.3.4 Relational operators.7
2.3.5 Bitwise operators.7
2.3.6 Conditional operators .7
2.3.7 Assignment .7
2.3.8 Constants .7
2.3.9 Functions.7
2.4 Default reference axis.8
3 MDS tools.8
3.1 Introduction .8
3.2 Schema tools.8
3.2.1 Introduction.8
3.2.2 Base types.8
3.2.3 Root element.8
3.2.4 Top-level types.10
3.2.5 Description metadata tools .18
3.3 Basic datatypes.21
3.3.1 Introduction.21
3.3.2 Integer datatypes.21
3.3.3 Real datatypes .21
3.3.4 Vectors and matrices .21
3.3.5 Probability datatypes .23
3.3.6 String datatypes.24
3.4 Linking, identification and localization tools .25
3.4.1 Introduction.25
3.4.2 References to Ds and DSs.25
3.4.3 Unique Identifier .26
3.4.4 Time description tools .26
3.4.5 Media Locators .29
3.5 Basic description tools.30
3.5.1 Introduction.30
3.5.2 Language identification .31
3.5.3 Textual annotation.32
3.5.4 Classification Schemes and Terms .37
3.5.5 Description of agents.49
3.5.6 Description of places .53
3.5.7 Graphs and relations.53
3.5.8 Ordering Tools.55
3.5.9 Affective description .56
3.5.10 Phonetic description. .67
3.6 Media description tools .67
3.6.1 Introduction.67
3.6.2 Media information tools.68
3.7 Creation and production description tools .73
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ISO/IEC TR 15938-8:2002(E)
3.7.1 Introduction.73
3.7.2 Creation information tools.74
3.8 Usage description tools .76
3.8.1 Introduction.76
3.8.2 Usage information tools .77
3.9 Structure description tools .78
3.9.1 Introduction.78
3.9.2 Base segment description tools .79
3.9.3 Segment attribute description tools.80
3.9.4 Visual segment description tools .87
3.9.5 Audio segment description tools.109
3.9.6 Audio-visual segment description tools .110
3.9.7 Multimedia segment description tools.113
3.9.8 Ink segment description tools.114
3.9.9 Video editing segment description tools .122
3.9.10 Structural relation classification schemes .129
3.10 Semantics description tools .133
3.10.1 Introduction.133
3.10.2 Abstraction model .134
3.10.3 Semantic entity description tools.134
3.10.4 Semantic attribute description tools .150
3.10.5 Semantic relation classification schemes .153
3.11 Navigation and access tools.157
3.11.1 Introduction.157
3.11.2 Summarization.158
3.11.3 Views, partitions and decompositions.184
3.11.4 Variations of the content .199
3.12 Content organization tools.202
3.12.1 Introduction.202
3.12.2 Collections .202
3.12.3 Models .208
3.12.4 Probability models .209
3.12.5 Analytic models .214
3.12.6 Cluster models.219
3.12.7 Classification models.220
3.13 User interaction tools .223
3.13.1 Introduction.223
3.13.2 User preferences .223
3.13.3 Usage History.235
4 Visual tools .240
4.1 Basic visual tools.240
4.1.1 Grid layout.240
4.1.2 Visual time series .240
4.1.3 2D-3D multiple view.247
4.1.4 Spatial 2D coordinates.251
4.1.5 Temporal interpolation.254
4.2 Color description tools.257
4.2.1 Color space .257
4.2.2 Color quantization .258
4.2.3 Dominant color .259
4.2.4 Scalable color .262
4.2.5 Color layout.264
4.2.6 Color structure.268
4.2.7 GoF/GoP color .279
4.3 Texture description tools .280
4.3.1 Homogeneous texture.280
4.3.2 Texture browsing.283
4.3.3 Edge histogram.286
4.4 Shape description tools .291
4.4.1 Region-based shape .291
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ISO/IEC TR 15938-8:2002(E)
4.4.2 Contour-based shape.294
4.4.3 Shape 3D .298
4.5 Motion description tools .302
4.5.1 Camera motion.302
4.5.2 Motion trajectory.307
4.5.3 Parametric motion .309
4.5.4 Motion activity.313
4.6 Localization tools.319
4.6.1 Region locator.319
4.6.2 Spatio-temporal locator .322
4.7 Other visual tools.329
4.7.1 Face recognition.329
Annex A Patent statements . 338
Bibliography .340
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ISO/IEC TR 15938-8:2002(E)
Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are members of
ISO or IEC participate in the development of International Standards through technical committees
established by the respective organization to deal with particular fields of technical activity. ISO and IEC
technical committees collaborate in fields of mutual interest. Other international organizations, governmental
and non-governmental, in liaison with ISO and IEC, also take part in the work. In the field of information
technology, ISO and IEC have established a joint technical committee, ISO/IEC JTC 1.
International Standards are drafted in accordance with the rules given in the ISO/IEC Directives, Part 2.
The main task of the joint technical committee is to prepare International Standards. Draft International
Standards adopted by the joint technical committee are circulated to national bodies for voting. Publication as
an International Standard requires approval by at least 75 % of the national bodies casting a vote.
In exceptional circumstances, the joint technical committee may propose the publication of a Technical Report
of one of the following types:
— type 1, when the required support cannot be obtained for the publication of an International Standard,
despite repeated efforts;
— type 2, when the subject is still under technical development or where for any other reason there is the
future but not immediate possibility of an agreement on an International Standard;
— type 3, when the joint technical committee has collected data of a different kind from that which is
normally published as an International Standard (“state of the art”, for example).
Technical Reports of types 1 and 2 are subject to review within three years of publication, to decide whether
they can be transformed into International Standards. Technical Reports of type 3 do not necessarily have to
be reviewed until the data they provide are considered to be no longer valid or useful.
ISO/IEC TR 15938-8, which is a Technical Report of type 3, was prepared by Joint Technical Committee
ISO/IEC JTC 1, Information technology, Subcommittee SC 29, Coding of audio, picture, multimedia and
hypermedia information.
ISO/IEC 15938 consists of the following parts, under the general title Information technology — Multimedia
content description interface:
— Part 1: Systems
— Part 2: Description definition language
— Part 3: Visual
— Part 4: Audio
— Part 5: Multimedia description schemes
— Part 6: Reference software
— Part 7: Conformance testing
— Part 8: Extraction and use of MPEG-7 descriptions
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ISO/IEC TR 15938-8:2002(E)
Introduction
This standard, also known as "Multimedia Content Description Interface," provides a standardized set of
technologies for describing multimedia content. The standard addresses a broad spectrum of multimedia
applications and requirements by providing a metadata system for describing the features of multimedia
content.
The following are specified in this standard:
� Description Schemes (DS) describe entities or relationships pertaining to multimedia content.
Description Schemes specify the structure and semantics of their components, which may be
Description Schemes, Descriptors, or datatypes.
� Descriptors (D) describe features, attributes, or groups of attributes of multimedia content.
� Datatypes are the basic reusable datatypes employed by Description Schemes and Descriptors
� Systems tools support delivery of descriptions, multiplexing of descriptions with multimedia content,
synchronization, file format, and so forth.
This standard is subdivided into eight parts:
Part 1 – Systems: specifies the tools for preparing descriptions for efficient transport and storage,
compressing descriptions, and allowing synchronization between content and descriptions.
Part 2 – Description definition language: specifies the language for defining the standard set of
description tools (DSs, Ds, and datatypes) and for defining new description tools.
Part 3 – Visual: specifies the description tools pertaining to visual content.
Part 4 – Audio: specifies the description tools pertaining to audio content.
Part 5 – Multimedia description schemes: specifies the generic description tools pertaining to multimedia
including audio and visual content.
Part 6 – Reference software: provides a software implementation of the standard.
Part 7 – Conformance testing: specifies the guidelines and procedures for testing conformance of
implementations of the standard.
Part 8 – Extraction and use of MPEG-7 descriptions: provides guidelines and examples of the extraction
and use of descriptions.
© ISO/IEC 2002 – All rights reserved vii
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TECHNICAL REPORT ISO/IEC TR 15938-8:2002(E)
Information technology — Multimedia content description
interface —
Part 8:
Extraction and use of MPEG-7 descriptions
1 Scope
This International Standard specifies a metadata system for describing multimedia content. This document
gives examples of extraction and use of descriptions using Description Schemes, Descriptors, and datatypes
specified in ISO/IEC 15938. The following set of subclauses are provided for each description tool, where
optional subclauses are indicated as (optional):
� Informative examples (optional): provides informative examples that illustrate the instantiation of the
description tool in creating descriptions.
� Extraction (optional): provides informative examples that illustrate the extraction of descriptions from
multimedia content.
� Use (optional): provides informative examples that illustrate the use of descriptions.
This document is meant to be a companion technical report for Part 5 (Multimedia Description Schemes) and
Part 3 (Visual) of ISO/IEC 15938. As such, the content of this technical report is not easily understood
without the technical specifications. In this technical report, effort has been made to preserve the specific
subclause numbering of ISO/IEC 15938-5 and ISO/IEC 15938-3 to allow easy correlation of the content on
extraction and use in the technical report with the technical specifications.
2 Terms and definitions
2.1 Conventions
2.1.1 Description tools
This part of ISO/IEC 15938 specifies the multimedia description tools as follows:
� Description Scheme (DS) – a description tool that describes entities or relationships pertaining to
multimedia content. DSs specify the structure and semantics of their components, which may be
Description Schemes, Descriptors, or datatypes.
� Descriptor (D) – a description tool that describes a feature, attribute, or group of attributes of
multimedia content.
� Datatype – a basic reusable datatype employed by Description Schemes and Descriptors.
� Description Tool (or tool) – refers to a Description Scheme, Descriptor, or Datatype.
2.1.2 Naming convention
In order to specify the multimedia description tools, this part of ISO/IEC 15938 uses constructs provided by
the Description Definition Language (DDL) specified in ISO/IEC 15938-2, such as "element", "attribute",
"simpleType" and "complexType". The names associated to these constructs are created on the basis of the
following conventions:
� If the name is composed of multiple words, the first letter of each word is capitalized, with the exception
that the capitalization of the first word depends on the type of construct as follows:
� Element naming: the first letter of the first word is capitalized (e.g. TimePoint element of TimeType).
� Attribute naming: the first letter of the first word is not capitalized (e.g. timeUnit attribute of
IncrDurationType).
� complexType naming: the first letter of the first word is capitalized, and the suffix "Type" is used at the
end of the name (e.g. PersonType).
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ISO/IEC TR 15938-8:2002(E)
� simpleType naming: the first letter of the first word is not capitalized, the suffix "Type" may be used at the
end of the name (e.g. timePointType).
Note that when referencing a complexType or simpleType in the definition of a description tool, the "Type"
suffix is not used. For instance, the text refers to the "Time datatype" (instead of "TimeType datatype"),
to the "MediaLocator D" (instead of "MediaLocatorType D") and to the "Person DS" (instead of
"PersonType DS").
2.2 Terminology
For the purposes of this part of ISO/IEC 15938, the following terms and definitions apply.
2.2.1 Schema-related terminology
2.2.1.1
Attribute
A field of a description tool which is of simple type.
2.2.1.2
Base type
A type that serves as the root type of a derivation hierarchy for other types.
2.2.1.3
Datatype
A primitive reusable type employed by Description Schemes and Descriptors.
2.2.1.4
Derived type
A type that is defined in terms of extension or restriction of other types.
2.2.1.5
Description
An instantiation of one or more description tools.
2.2.1.6
Description Scheme
A description tool that describes entities or relationships pertaining to multimedia content. Des
...
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