=Paper=
{{Paper
|id=Vol-2519/paper2
|storemode=property
|title=A Knowledge Organization System for Image Classification and Retrieval in Petroleum Exploration Domain
|pdfUrl=https://ceur-ws.org/Vol-2519/paper2.pdf
|volume=Vol-2519
|authors=Mara Abel,Eduardo Simões Lopes Gastal,Cassiana Roberta Lizzoni Michelin,Luiza Gonçalves Maggi,Bruno Eduardo Firnkes,Felix Eduardo Huaroto Pachas,Renata dos Santos Alvarenga
|dblpUrl=https://dblp.org/rec/conf/ontobras/AbelGMMFPA19
}}
==A Knowledge Organization System for Image Classification and Retrieval in Petroleum Exploration Domain==
https://ceur-ws.org/Vol-2519/paper2.pdf
A knowledge organization system for image classification and
retrieval in petroleum exploration domain
2 2
Mara Abel1, Eduardo S. L. Gastal1, Cassiana Michelin , Luiza Gonçalves Maggi ,
Bruno Eduardo Firnkes1, Felix Eduardo Huaroto Pachas1, Renata dos Santos
2
Alvarenga
1Informatics Institute, 2Geosciences Institute - Universidade Federal do Rio Grande do
Sul (UFRGS) PO 15.064 – 91.501-970 – Porto Alegre – RS – Brazil
{marabel,eslgastal,bruno.firnkes}@inf.ufrgs.br,
cassiana.michelin@ufrgs.br
Abstract. This paper describes a knowledge organization scheme for types of
images in the domain of petroleum exploration based on ontological criteria.
The classification separates the characteristics of representation, visualization,
and storage from the semantics of the content, where each of these features has
its proper organization system. The representation and visualization classes
optimize the effort of image annotation by grouping the instances of images
according to a set of criteria described in this paper, which are more directly
identified by automatic classification methods. These classes keep a relationship
with the geological entities depicted in the images. In this way, we can separate
training methods for identifying the type of representation and for those that
learn about what geological entity this representation is. In this ongoing project,
we apply the knowledge organization for information retrieval over a set of 1927
images related to petroleum exploration. Preliminary results show good
accuracy in simple classification tasks and indicate the need for improved
classifiers in complex tasks, where the proposed ontological system will be
fundamental for organizing the image datasets.
1. Introduction
The recent growth in technologies for image creation and processing has greatly
simplified the production of visual records and pictorial representation of pieces of
geological evidence that supports exploration activities. This evolution results in a
growing amount of digital visual content available in unstructured and non-indexed forms
on data repositories that support the interpretation and decision taking by geologists and
reservoir engineers. In the production of geological models, the geologist needs to access
the previously produced maps, photographs, and many types of graphical visualization of
analogic measures in order to support and explain her/his process of interpretation. The
selection of relevant material should consider their type, scale of analysis, generator
activity, and semantic content. However, even in the petroleum industry, the evolution of
the image organization systems have not provided the adequate conceptual tools to help
in the development of the image-retrieval computing systems to cope with the variety of
types of geological visual content. The machine-learning techniques did lack a supporting
classification system that organizes the sets of images and helps in reducing the number
of samples required for learning these types.
An image classification system fitted to geological content would improve the
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
nowadays techniques of image indexing based on manual annotation or content-based
retrieval. The manual annotation approach associates keywords to the image in the time
it is stored, and it shows the best results for semantic retrieval. However, the approach
has intrinsic limitations. The first is that human annotation is subjective, so it does not
offer a homogeneous classification system for indexing that allows the user to recover
similar images. The images in Figure 1 are both about normal geological faults. The
Figure 1(a) is a 3D diagram block that schematically represents a geological normal fault,
while the Figure 1(b) is a picture of a geological normal fault in Thingvellir rift, Iceland.
A person can annotate the Figure 1(a) as 3D diagram (a type of visualization) or
geological normal fault (the represented entity) or a divergent zone (an interpretation
about the cause of this geological occurrence). Figure 1(b) can be annotated as a
photography, a landscape or a geological normal fault, a lake, etc. When very large sets
of images (in the order of millions of images) are available for training, linear scalable
machine learning algorithms shows some impressive results, as the Google image
retrieval system [G. Chechik 2009]. However, in expertise interpretation, the number of
collected images is low considering the variety of represented entities and, moreover, the
user wishes to retrieve visual objects in a more abstract level that has no immediate
translation for physical attributes of the image [Abel, Mastella, et al. 2004].
Figure 1 (a) A 3D diagram block depicting a normal fault. (b) A photograph that
shows a normal fault (picture of National Geographic Society).
Our classification system offers to the person the possibility of classifying the
types of visual content in separate of the visual content itself. We aim to provide a uniform
way of annotating images and producing a previous classification of the visual content
based on image features that allow the development of fitted algorithms of image
processing and machine learning that can learn over a small set of images and figures.
The approach will be further used to retrieve the large set of unlabeled images stored in
corporate databases.
The further sections of this paper describe, in section 2, a review of related works
in ontology-based image indexing, while section 3 reviews the conceptual basis for
modeling of visual content. Section 4 presents the basis of our classification system and
details the main classes and dimensions utilized for indexing. Finally, section 5 describes
the preliminary results of automatic image classification that applies our system, ending
with conclusions and next planned steps.
2. Related work on ontology-base retrieval systems
Several recent works are exploring the use of ontologies to help the visual content
extraction in information retrieval for imagistic domains. Pandey and colleagues [Pandey,
Khanna et al. 2016] offer a relevant review on techniques for combining image extraction
and semantic processing to deal with the user intention in recovering visual content. Also,
Zin and colleagues present a systematic review on content-based image retrieval
specifically for the medical domain [Mohd Zin, Yusof et al. 2018]. According to them, a
content-based image retrieval system applies one of the following approaches to deal with
the semantic gap between image and meaning of content: (1) a domain ontology that
reduces the search space, (2) machine learning algorithms, for large databases with the
uniform type of images; (3) relevance feedback of user; (4) semantic template generation;
(5) combined textual and visual content of images. According to them, the combination
of image extraction and conceptual models is the most challenging approach nowadays
and strongly depends on the quality of metadata. The works of [Tian 2016; Chen and
Chen 2017; Gonçalves, Guilherme et al. 2018; Kuang, Yu et al. 2018] apply the merge of
techniques. We understand that for restricted knowledge-intensive domains, such as
petroleum geology, the support of separate domain ontologies and image feature
ontologies may increase the accuracy and relevance of the retrieved content. The followed
works also apply this approach.
Sharm and Siddiqui [Sharma and Siddiqui 2016] describe an ontology-based
framework for retrieval of museum artifacts. As we do, the authors propose a domain
ontology with the representation and visualization aspects of the domain - analysis
ontology – in separate of the domain ontology that defines the museum artifacts – domain
ontology. The approach starts by segmenting the input image and extracting low-level
descriptors of the segments and their relation to the concepts of domain ontology. The
researchers have tested the approach with 1200 images from 11 categories. The images
were pre-processed to uniformize their type, size, contrast and noise content.
Santos Neto [Neto 2013] proposes the ontology ONTOLIME to support
information retrieval with medical images. As we propose in our approach, the author
models the physical aspects of the images, such as technical capturing process, color, and
texture, as subclasses aspects of the Image concept, while model medical concepts (such
as Anatomy) has its proper subclasses related to the knowledge domain.
Even though there is a large effort in developing domain ontologies for the
subdomains of petroleum exploration and the importance of visual content in this domain,
few works were published reporting the use of Geology ontologies for image retrieval. In
her dissertation [Barreiros 2010], Barreiros proposes an ontological model for outcrop
description that relates the image content produced by a geologist in the fieldwork.
However, the property and types of images are not detailed in the ontology, and neither
are the methods used for image retrieval. More relevant is the effort of industry and
organizations in producing image-based retrieval systems for geology. Endeeper has
produced the portal PetrographicPedia 1 for image retrieval of petrographic images [Castro
2012] and the content-based image indexing system RockViewer2 using the Petrography
ontology of Petroledge [Abel, Goldberg et al. 2013]. In both systems, the geologist
describes the image in the moment of capturing using a controlled vocabulary based on
the ontology. The CPRM (Geological Service of Brazil) offers access to a large set of
geographical maps and geological images through its exploration and production database
BDEP, where a local thesaurus of geosciences [Nascimento and Freire 2005] supports the
content organization. In addition, the C&C Reservoir Company3 keeps a large database
of classified images, based in an organization system that mixes content, interpretation
1
www.endeeper.com/petrography-pedia-en-us/Main_Page
2
www.endeeper.com/products
3
www.ccreservoirs.com
and image artifact types.
3. Previous work on conceptual modeling of visual content entities
Even being intrinsically connected, images and visual content are disjoint concepts. In
order to understand the distinction, we refer to the work of Lorenzatti, which proposed a
distinction on existent objects, concepts, representations and visualizations [Lorenzatti,
Abel et al. 2009]. An object is supposed to be an existent entity in reality, which can be
an abstract entity (such as an emotion, or the number 5), a social entity (an enterprise) or
a concrete instance (a dog). A concept, otherwise, is a mental abstraction of a portion of
reality, emphasizing the aspects of entities that are the interest of the human observer. A
representation is one of the many possibilities in wha person externalize the concepts to
share her/his conceptualization among the community. A representation serves to the
communication process between a sender, the interpreter of reality, and a receiver that is
part of the community. Finally, visualization refers to the process of creating a pictorial
expression of the concept to help the receptor to gain insight or understanding of the
sender.
Moreover, the work of [Perrin, Rainaud et al. 2013] defines, as shown in Table 1,
the meaning of model, representation, and visualization in the context of petroleum
geological modeling. We consider these meta-types of information to propose our
approach and separate the modeling of geological objects from those of its representation
and visualization artifacts. Representations and visualization are informational entities.
In YAMATO, [Mizoguchi and Toyoshima 2006] consider that a representation
artifact is composed of (representation) form and content, and a representing thing is
composed of a representation asset and a representation medium. Visualizations are the
representation form of some content that express a geological concept or its individuals.
The Information Artifact Ontology (IAO) [Smith and Werner 2015] offers a
domain-neutral resource to represent information content entities, such as documents,
databases and digital images. The authors derive the ontology from BFO definitions [Arp,
Smith et al. 2015]. For IAO, an information content entity is generically dependent on
some material entity (in BFO sense) that retain a relation of aboutness to some entity.
According to the authors, aboutness can be considered a reference relation that includes
the relations of cognitive and intentional directedness involved in the capturing of
information. Our model will refer to these uplevel concepts.
Table 1 – Definition of the geological modeling artifacts extracted from [Perrin,
Rainaud et al. 2013]
Abstracts a portion of reality according to the conceptualization of some
Model observer.
Is conceived according to some explicit theory known by the modeler.
Rests on a definite symbolism restricted by a representation language. Is
connected to one model.
Representation
The theory that underlines the model is incorporated into the representation.
Many representations can be associated with a single model.
Relies on the human visual system to perceive the modeled information
Includes some parameters associated with the observer or to the conditions of
Visualization
observation.
Many visualizations can be associated to one representation.
We believe that the understanding of the domain to propose the organization of
image and content ontologies can improve the indexing and retrieval of domain images,
then we based our organization system in the understanding of the domain methods and
information for geological interpretation. This analysis is in section 4.
4. Criteria for image classification in petroleum exploration
The main contribution of this work is to clarify the criteria applied for visual content
organization in petroleum domain and build the domain ontology for labeling or image
artifact indexing. For a question of space, only the main classes of the ontology were
described here while the quality attributes derived from the listed criteria and the is about
relations were omitted.
In the following of this section, we describe the criteria applied for the visual
content organization for exploration domain in our work. We propose the dimensions of
analysis of the data and describe their meaning in Tables 2 to 7. On section 5, we formalize
the entities deriving from IAO ontology.
4.1 Criterion Scale of Analysis
In petroleum exploration, the scale of analysis is one of the more important organization
dimension. The scale of analysis considers both the dimension of the object of
investigation, as well as, the scope of geological study. Our proposal is based on the works
of [Fávera 2001; Jarna, Bang-Kittilsen et al. 2015]. The investigation of new economic
assets starts on the study of the continental situation of the sedimentary basins at a very
large scale, where the object of analysis spreads around 10 7 meters and proceeds with
growing detail until the image acquisition of rock samples in nanoscale (10 -6 meters).
The data in continental and regional scale come from Government, academy or public
agencies and support the decision of the target areas that will be licensed by companies.
From this license on, the detailed studies that will produce data and images will be carried
on by the company. Table 2 describes the scales of analysis and target objects in the
exploration activities.
Table 2 – Indexing nomenclature for the scale of analysis
Name of Order of
Geological/engineer concepts Visualizations
scale magnitude
Continental Continent, tectonic plate, tectonic Continental map, diagram of plate
scale 107 meters structure tectonics, structural map
Regional map, paleogeography
Sedimentary basin Depositional
Basin scale 105 meters system Tectonic system
map, diagram of tectonic and
sedimentation interpretations
Petroleum field, regional stratigraphy, Local map, geological map,
Field scale 103 meters and geological formations, stratigraphy diagram, seismic
depositional environment section
Geological 3D model, diagram
Geological formation, Regional
Reservoir block, seismic cube and section,
scale 100 meters Stratigraphy, geological formations
stratigraphy diagrams, grid
tectonic structures
model, flow model
Geophysics well log, lateral well
Sedimentary facies, vertebrate fossil,
imaging, diagram block,
Outcrop rock layer, tectonic structure,
scale 10-1 meters geological formation, depositional
geological 3D model, columnar
diagram, well pictorial
geometry
descriptions, pictures
Macroscopic Lithology, sedimentary structure, Pictures, diagrams, graphical
scale 10-3 meters fossil. Well cores and hand samples plots
Lithology, sedimentary structure,
microfossil, grain and minerals,
Microscopic Pictures, diagrams, graphical
scale 10-6 meters chemical composition. Thin section,
plots
cuttings, earth material for chemical
analysis
Grain and crystal aspects, mineral
Pictures, diagrams, graphical
Nanoscale 10-9 meters composition, disaggregate rock
plots
samples
4.2 Criterion Type of Visualization
Each of these scale analysis produces a large variety of types of visualizations for the
geological objects. The Table 3 list here the type of visualizations found in petroleum
exploration and their description. We consider that our classification system covers all
the visualization found in the geology domain and then can be used to segment the whole
set of available visualizations in a corporate database to support specific methods of
content extraction.
Table 3 – Types of visualization for geological objects
Visualization type Description
Diagrammatic visualization of the geographic distribution of some measure
1 Map
or information in plant
Diagrammatic visualization of the vertical slices of the Earth in some
2 Cross Section
particular scale of analysis.
Diagrammatic visualization of the vertical slices of limited lateral portions
3 Profile of the terrain. A profile emphasizes the vertical variation of the geological
features, which distinguishes it from cross-sections.
It is a 2D or 3D visualization of the interpreted distribution of rocks and
4 Geological Model geological structures in the subsurface. It is conceived to be produced by
computer systems.
An image captured by some equipment that can register the variances of
5 Photograph
light over the object or the scene.
Combined visualization of several distinct geological data related to a
6 Chart single variable (usually geological time) to help the geologist to get an
integrated comprehension of the data.
A schematic or simplified graphical representation of some geological
7 Diagram
feature
A graphical representation of a data set showing the relationship between
8 Graph
two or more variables
Hand-made draw that tries to keep some spatial correlation with an existing
9 Sketch
geological feature or scene
4.3 Criterion Methods for information acquisition
Besides scale and types of visualization, the geology method that produces the images
also provides useful information for retrieval. Table 4 lists the main techniques utilized
to produce information (textual and visual content) to support petroleum exploration.
Table 4 – Techniques and methods for information acquisition in exploration
Information acquisition Scale of
Visualizations
technique analysis
Aerophotogrametry/ Satellite Aerial photograph, physical geography map, structural
Continental
image capturing map
Radar Continental Radar image, Physical geography map, Structural map
Gravimetry study Continental Isopach map
Magnetic study Continental Isopach map
Geological regional mapping Basin Geological map, stratigraphic chart, photograph
Geological map, stratigrafic chart,
Field studies Field
photograph, paleontology chart, outcrop sketch
Field Subsurface map, cross section, profiles, cube, structural
Seismic exploration
/reservoir subsurface map and cross section, stratigraphic chart
Geophysic log, stratigraphic chart, profile, stratigraphic
Reservoirs/
Well perforation chart, core description, well-cutting log, borehole
Outcrop
imaging, core box photograph, sample photograph
Field Subsurface map, cross section, profiles, cube, structural
Seismic exploration
/reservoir subsurface map and cross section, stratigraphic chart
Geophysic log, stratigraphic chart, profile, stratigraphic
Reservoirs/
Well perforation chart, core description, well-cutting log, borehole
Outcrop
imaging, core box photograph, sample photograph
Geochemical analysis Macroscopic Graphic plot, compositional map
Lithology description, microphotograph, graphic plot,
Petrographic analysis Microscopic
lithology chart
Tomography/ spectrography
Nanoscale Photograph, graphic plot
analysis
4.4 Criterion entity accessibility
The professional can capture the data from direct and indirect observation (Table 5).
Direct observation refers to any of the representations and their visualizations produced
by a person who was able to have direct sensorial access to the geological object to
produce a representation. Indirect observation refers to the representations and
visualizations produced from data capture by analog devices, such as seismic, well logs,
spectrograph, and tomography analysis.
Table 5 – Type of observation of the geological entity.
Geological/engineer
Type of observation Visualizations
concepts
Photographs, core description, petrographic
Direct observation Outcrop, core, rock sample plots, aerial photograph
Structure, geological Seismic profile and sections, seismic cubes,
Indirect observation
unit, reservoir geophysical logs, radar images, isopach maps
4.5 Criterion location
Since Geology deals only with concrete entities, the location dimension is of central
importance in information organization. Several distinct reference systems index
information contents regarding location. Considering altitude/depth reference (Table 6),
the data can come from the surface (maps, gravimetrical measures, geographic and
geological maps, pictures) or subsurface data (seismic sections and maps, and geophysical
well logs). In some situations, examples of visualizations may repeat themselves in
surface and subsurface location. The more important index for general geological
information is the geological and geographic location of the geological entities.
Considering the maturity of the ontology-based systems for location, we index the visual
content in this work with support of the previously existing systems for geographical
position (such as, latitude and longitude, UTM coordinates, and other reference
coordinates), geographical and geological location organized by the Open Geospatial
Consortium (OGC) [OGC 2015]. For the geological location, we only consider the
possible locations of petroleum relevant occurrences, which means sedimentary terrains.
For the geoeconomic location, we adopt the Glossary of the Brazilian Petroleum Agency
(ANP) [ANP 2016]. Finally, a particular case for geological location is the
chronostratigraphic scale, which studies the position of rock bodies in relation to time.
Table 7 summarizes the geolocation systems that we adopted in this work.
Table 6 – Indexing nomenclature for geological object location in
surface/subsurface.
Vertical Geological/engineer concepts Visualizations
location
Continent, region, sedimentary Maps, columnar sections, gravimetric records,
Surface basin, field, outcrop, isolate rock stratigraphic and compositional charts, diagrams
sample and photomicrography
Seismic profile and sections, seismic cubes.
Geological units (host rocks and
Subsurface Well logs, wall imaging of the well, diagrams
reservoir) , well cores
and photomicrography
Table 7 – Adopted standard system and indexing nomenclature for geological
object location in surface
Geolocation
systems
Adopted standard or nomenclature
(horizontal
location)
Geographical The OGC standard for geographic information coordinate systems [OGC
position 2015]
Geographical The OpenGIS Standard of OGC , for places.
location location
Geological Continent, craton, basin, formation, layer and their subdivisions. OGC
Geoeconomic Sedimentary basin, petroleum or gas field, block, reservoir. The ANP Glossary
location [ANP 2016]
Chronostratigraphic
location of Eonothem, Erathem, System, Series, and Stage
geological units
Besides technique and context, we need to classify the visual content according to
the organizational origin of the data. Which sector of the company or external agent has
produced the data and what is the organizational function of these agents? The geologist
complements the exploration investigation with the studies of analogous, i.e., description
of other reservoir occurrences in the world that keeps similarities with the target area and
can bring useful lessons. In this work, we will not detail the classification system for
organizational and external agents, since they are tightly related to the managerial
organization of the companies.
Our work proposes an image classification system that considers each of the above
aspects of information, which complements the geological domain ontology that
describes the content of the images and pictorial representations.
5. Ontology modeling
We modeled the described entities by specializing the information content entity of the
Information Artifact Ontology (IAO) [Smith and Werner 2015] available in Ontobee 4
[Xiang, Mungall et al. 2011]. Figure 1 intends to show what concepts were derived from
IOF in order to cover the whole classification systems described in the previous sections.
The derived nine geological visual entities have the names underlined in Figure 1. Each
one of these entities keeps an aboutness relationship with predefined geological entities.
The geological entity will be of one of the following types of material entities- rock,
geological object – or generically dependent continuant - geological structure or
specifically dependent continuant - geological contact. For reasons of space, it is not
included in this paper the classes of geological objects and engineering objects that keep
the aboutness relationship with the information content entities. In this stage of the
research, only the visual content classes that we explain in this article were applied to
classify the images and help the user to retrieve the image by the criteria described in
section 4.2 to 4.5.
Figure 1 – Specializations of the Information dependent entity of IAO.
6. Content extraction
As a starting point, we have manually annotated, using the classification system, a dataset
containing 261 images. The initial set includes images on two categories: (a) 128 images
of type “Photograph” containing a variety of sub-types that include visual textural
information of rocks (including photos of outcrops and cores); and (b) 133 images of type
4
http://www.ontobee.org/ontology/IAO?iri=http://purl.obolibrary.org/obo/IAO_0000030
“Maps” containing examples of many of its sub-types. Figure 2 shows examples of
photographs, and Figure 3 contains examples of maps from this dataset.
For each image, we computed a feature number based on the mean-squared-error
deviation from a straight line of the image’s log-histogram of horizontal derivatives
(approximated by a convolution with the filter [1 -1]). Figure 4 shows preliminary results
for each type of visualization. We then defined a binary classifier to divide automatically
the images onto the two categories described above by hard thresholding. The threshold
was selected by a simple brute-force search, where the objective function was defined as
to maximize the accuracy of the classifier. This simple proof-of-concept obtained a
precision of 97% (98 true positives and 3 false positives) with a recall rate of 76%.
Figure 2 – Photographs of well cores, outcrops, and sedimentary structures.
Images from [d'Avila et al. 2004; Castro et al. 2005; Szatmari 2005; Magnavita et
al. 2005; Moraes et al. 2006; Santos et al. 2008].
With this promising initial result, we moved to more challenging instances of the
problem. We manually annotated a dataset of 1927 images from the domain of petroleum
exploration using the proposed ontology. This dataset is composed of eight classes:
seismic sections (248 images), geological maps (685 images), 3D block diagrams (100
images), profiles (99 images), thin sections (100 images), lithostratigraphic charts (170
images), portraits (164 images), and photographs of rocks (361 images).
For each image in this dataset, we computed a 253-dimensional real vector of
features, composed of: (i) the 125 values of the image’s RGB color histogram, computed
by dividing the 3D RGB space in 5 bins in each dimension (5×5×5 = 125); and (ii) the
first 128 values of the histogram of the image’s horizontal derivatives magnitudes (that
is, the absolute value was taken after applying the filter [1 -1] described above). Both
histograms were normalized by dividing each bin count by the maximum bin count.
Figure 3 – Instances of maps in several scales and techniques of analysis.
Images from [Júnior et al. 2004; Arai and Lana 2004; Ponte and Asmus 2004;
Cupertino and Bueno 2005; Daudt et al. 2009]
Figure 4 – Four examples of visualization with the respective log-histogram of
horizontal derivatives that shows a particular pattern for each visualization type.
With the described features, we trained a Logistic Regression multi-class classifier
with a simple L2 penalty. For the loss function, we balanced the weight given to each
class based on the total number of images per class, to avoid biasing the classifier to the
most frequently occurring classes. We performed 50 runs using 10-fold cross validation,
with random permutations of the dataset, and obtained a mean accuracy of 59% in the
classification (a good improvement over the expected accuracy of 12.5% for a uniformly
random classifier, assuming a uniform prior on the class distribution).
Minimum/Average/Maximum precision and recall rates are, respectively, 15%/54%/83%
and 34%/57%/75%.
These numbers show that there is a lot of room for improvement in the
classification. We are now exploring non-linear classifiers based on Neural Networks,
where a large and correctly annotated dataset becomes essential. This method will be
complemented by the annotation of the geological objects on the images and exploration
of the aboutness relation on the automatic classification algorithm. In this direction, the
proposed ontological organization system is a key part for simplifying and guiding the
annotation process.
7. Conclusion
We presented here a knowledge organization system for the classification and indexing
of visualizations of geological content in petroleum exploration domain. We investigate
the several types of visual content utilized in petroleum exploration, and we propose a
limited number of classification criteria and the associated visual content classes to
support image organization in corporate systems. Our approach has the benefit of the
ontological analysis of the object of representation (the visual artifact), as well as the
understanding of types and techniques for expressing the semantic content (the geological
object that the artifact is about). We consider that this multi-classification system will
reduce the need for a large number of images in training sets on the visual content
extraction in the domain. Preliminary results in visual-content extraction showed that,
with support of the organization system, we reduce some complex steps of image analysis
that allows applying basic image processing algorithms.
Acknowledge: We carry on this project in collaboration with Petrobras. We acknowledge
the support of Brazilian sponsor agencies CAPES and CNPQ.
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