An Ontology-Based Approach for Visual Knowledge: Image Annotation and Interpretation

Luís A. Lima Silva1*, Laura S. Mastella1, Mara Abel1, Renata M. Ga-lante1 and Luiz F. De Ros2

1 Instituto de Informática, UFRGS, Av. Bento Gonçalves 9500, Campus do Vale, Agronomi-a, Porto Alegre, Brazil, CEP: 91501-970

{llima, mastella, marabel, galante}@inf.ufrgs.br 2 Instituto de Geociências, UFRGS, Av. Bento Gonçalves 9500, Campus do Vale, Agrono-

mia, Porto Alegre, Brazil, CEP: 91501-970 lfderos@inf.ufrgs.br

Abstract. Ontology -based content annotation of images allow to semantically describe the visual objects that are found in an image, giving a standard and ex-plicit meaning to then, and also helping to answer queries under image reposito-ries. However, the challenge for annotation tools is to capture more abstract and interpreted image features (like evidences, findings, important observations, and so on) than concepts, attributes and values, which are atomically select from the ontology . For this reason, we proposes two conceptual annotation levels: a) one based on the selection of atomic concepts that users can identify and describe from the image and b) other introducing a knowledge-based model to describe visual features according to different annotation strategies and levels of granular-ity, trying to make explicit the visual knowledge that users effectively require to develop knowledge-based interpretation tasks of image. This approach is mainly exemplified by an ontology -based application for description and inter-pretation of oil-reservoir rock images. The visual chunk model is used to repre-sent visual patterns of domain concepts that are select from the oil-reservoir de-scription ontology , enabling to define important evidences, and connect them to interpretations using an evidence-interpretation model called K-graph. Finally, a developed tool allows to inspect the concepts from the ontology during the de-velopment of K-graphs and visual chunks. In summary, it is outlined a two level approach that is primarily explored in the oil-reservoir domain, but that can be reused in other domains where the visual knowledge annotation and reasoning is the key activity.

* This essay is supported by the Council of Research– CNPq through the founding program

CTPETRO – Process Number: 502009/2003-9.

1 Introduction

In the perspective of Artificial Intelligence - AI, an image is knowledge. Although there is a large number of knowledge-based problems dealing directly with images, the knowledge that an image can retain is not explored because it is not explicitly repre-sented. Recently, many approaches to ontology-based annotation of image content have been presented [1-4], which are mainly based on a standardized domain vocabu-lary where concepts, attributes and values are used to semantically characterize visual objects that have been determined visually. According to these approaches, an ontol-ogy is explored to semantically annotate an image or group of images, providing the information that is necessary to more precisely answer different types of query over image repositories. However, those proposals are focused on image descriptions that are developed using the same annotation structure – the ontology structure. Even knowing that this structure allows users to browse and select different domain con-cepts from the ontology, which provides a framework where users can choose image concepts in many levels of detail, the semantic content of an image is frequently de-scribed according to one annotation level only – the selection of atomic concepts. However, the image content must be semantically captured according to different annotation strategies and representation models , helping users to semantically de-scribe visual features at different levels of granularity and also, reflecting the different level of expertise of users in recognize the image content.

In this paper, we identify the necessity of two conceptual levels for image descrip-tion, following the ideas of acquisition and representation of visual knowledge pre-sented in [5]. First, it is necessary to describe the simplified visual objects, easily rec-ognized by a novice worker in the domain and that can be semantically characterized using atomic concepts, attributes and values from a domain ontology. In a second level, we describe more abstract and interpreted visual features, which are those rec-ognized by an expert, allowing not only to use these abstract visual patterns to de-scribe the image content and answer queries, but also to effectively solve knowledge-based tasks of image interpretation.

According to [6], interpretation means analysis of data to determine their meanings. According to [5], knowledge-based interpretation of images deals with the matching of more abstract and diagnostic visual features than low-level objects. For this reason, ontology-based content annotation of basic geometric visual features can only weakly help the extraction of more semantic and relevant inference methods of image interpre-tation. In addition to atomic ontology-based annotation approaches of visual objects, we are introducing that users must be faced with high-level annotation approaches, trying to capture more abstract visual information such as evidences, findings, inter-pretations, important observations and so on. The annotation of these kind of visual knowledge is crucial to develop knowledge-based systems for different applications, such as : ontology-based content description and interpretation of X-ray images in industry or medical domains, interpretation of human diseases in biochemical assays via ontology-based content annotation of microscopic images and, our the application that is the goal of our project: ontology-based description and interpretation of oil-reservoir rock images in petroleum exploration.

The description and interpretation of rock images is a problem concerned with the seeking of reasonable explanation for the formation process of an oil-reservoir rock, in the domain of sedimentary petrography. It is carried out by the analysis of the rela-tionship of rock features that are discerned in a naked-eye analysis under an optical microscope. These features, collected by some visual pattern-matching process, are selected/combined – using what was described by [7] as tacit knowledge – and only afterwards named and organized for incorporation into an explicit body of knowledge – an ontology. The challenge for our analysis is the effective ontology-based descrip-tion of the visual knowledge that experts both quantitative and qualitatively apply to evaluate a geological unit as an oil-reservoir. Besides, based on different annotation structures, we are proposing distinct annotation levels, which are related each other to enable the development of query and reasoning approaches under some image con-tent which is symbolically characterized.

Our approach uses more abstract and cognitive structures to be the primitive for ontology-based representation of visual knowledge, which are mainly defined as vis-ual chunks, following the ideas firstly studied by [8, 9]. A visual chunk is introduced in [5] as a new type of visual modeling concept for knowledge-based interpretation pur-poses, defined as an aggregation of rock concepts to represent geometric features that are specified in a domain ontology. What we are proposing is that the visual chunk modeling concept can also be used as a way of organizing annotation frameworks, allowing to describe the visual knowledge that experts apply for driving useful infer-ences in some domain application.

The visual knowledge acquired and annotated by visual chunks are most conven-iently associated with high-level interpretations using knowledge graphs [10]. A knowledge graph (K-graph) can be understood as a knowledge acquisition and repre-sentation model, which we are exploring to knowledge-based interpretation of images, having great expressivity and bigger granularity when compared to other formalisms that associate items of evidence with hypotheses, such as production rules or bayes-ian nets. Visual chunks and K-graphs are the main components of our ontology-based approach for visual knowledge annotation and reasoning, allowing to describe more semantic methods of interpretation, primarily based on the oil-reservoir domain, but also outlining an annotation and reasoning approach that can be reused in other do-mains where application of visual knowledge is the key activity.

The paper introduces two conceptual levels for ontology-based content annotation of images, firstly presenting the low-level image annotation to show the connection between visual objects and concepts-attributes-values that are atomically selected from the domain ontology. After that, the visual chunks and k-graphs modeling and reasoning structures are described, enabling users to define high-level visual informa-tion using an ontology-based annotation tool, and also allowing to introduce a rock interpretation method. Finally, our approach is discussed in relation to others.

2 Conceptual Levels for Ontology-Based Annotation of Images

In our work, we propose the specification and refinement of a set of knowledge models, such as knowledge engineering models [11-13], where visual knowledge ac-quisition and representation approaches are explored to help users in the semantic description of more abstract visual information than only simplified visual objects. Instead of only having one annotation level, the user is faced to two conceptual anno-tation levels : a low-level of annotation where users explore the annotations that are required to precisely find an image for a particular purpose, and a high-level of annota-tion where they are also interested on the description of visual aggregations (e.g. visual patterns) that are the required knowledge to support the development of rea-soning tasks.

As a general example of visual patterns, users could be interested on describing re-lationships among objects in a human face, searching to annotate and interpret a set of visual patterns to describe emotions, or semantically describe visual objects being captured from different frames of video, allowing to annotate and interpret temporal facts and events. In our example of visual pattern that is symbolically characterized, the spatial relationships among visual rock features, as well as the domain-specific relationships among rock concepts, the lists of attributes and the values, are logically related to capture an indivisible piece of information, which is an evidence to some interpretation. Finally, these levels are based on different annotation approaches, which are mapped each other to present an ontology-based annotation framework where different users can gradually specify and refine the annotations, allowing to adapt the expert-level knowledge to novice-level knowledge.

2.1 Low-Level of Image Annotation: The Oil-Reservoir Description Ontology Having introduced our two level annotation approach, we will describe this work

using one ontology-based application – the knowledge-based description and inter-pretation of rock images in the oil-reservoir domain. The oil-reservoir description on-tology that was developed in our project is mainly used to guide the image annotation process, providing a model where users explore the domain relationships among visual concepts which are symbolically represented, as well as their large sets of properties and values.

This domain ontology has provided a first important step to standardize the domain terminology explored during the description of sedimentary rock images, helping the complete and correct description of the different visual features using domain con-cepts, attributes and values. To this approach, the first annotation level is basically structured by this domain ontology, which was used to build a domain-oriented intel-ligent database application to description and interpretation of rock images [14].

Fig. 1 presents part of the oil-reservoir description ontology, which is described us-ing the RDF(S) graph representation of the RDF(S) Description Language [15]. As a matter of space, just one concept of the ontology is being presented, which is the “Diagenetic Composition” concept, described as a resource in RDF(S). In addition, we are presenting three of the several attributes used to specify a “Diagenetic Composi-

tion”, which are “Constituent Name, Location and Habit”. These attributes are repre-sented as RDF(S) properties of the resource, where the possible values to each one of the attributes were defined in a RDF(S) structure called bag, which is a container for an unordered list of elements. For instance, the “Habit Type” bag contains a list of all the possible values that can be assigned to the “Habit” attribute. Finally, the values are represented as RDF(S) literals.

Fig 1. Part of oil-reservoir description ontology that is represented in a RDF graph. The concepts, attributes and values modeled are the constructs necessary to build the k-graphs and visual chunks, which are presented in the following section.

Fig 2. This picture presents only the rock features in a low-level annotation approach, which are directly extract from the domain ontology. Here, we are describing the rock features that are related to Illite and Iron Oxides/Hydroxides visual chunks.

Using this ontology-based annotation level, users browse the concepts, attributes and values from the ontology, where multiple image descriptions can be produced according to different task requirements. Fig 2, for example, is presenting some of the visual objects that can be found using this rock image. Among other visual informa-tion that the user could annotate, only two sets of concepts, attributes and values are being described because the user are only focusing the annotation that is required to solve a task of sedimentary environment interpretation (a typical reasoning task of this domain). These concepts, attributes and values that are linked to this image were atomically selected from the oil-reservoir description ontology, where the user may find the appropriate oil-reservoir term to semantically describe the visual objects of interest.

2.2 High-Level of Image Annotation and Reasoning: Description of Oil-Reservoir

Patterns and Forward Rock Interpretation This approach for high-levels of image annotation and reasoning is based on the

important assumption that ontology-based content annotations of simplified features taken individually by users can only weakly help the extraction of useful inference methods. These reasoning methods are supported on more abstract and cognitive structures of visual knowledge, which are acquired and annotated using the visual chunks, are the effective evidences used to reach some interpretation. Therefore, the causal relationship between evidences and interpretation must be described, and we are using K-graphs to develop this task.

The K-graph is a tree where a) the root node describes the interpretation hypothe-sis, which is a broad visual information that is extracted from the analysis of an image, and b) the leaf nodes contain visual chunks identified by the experts in the image of rock as pieces of evidence necessary to support the interpretation. In terms of reason-ing under this annotations, each interpretation is associated in the K-graph with a

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Diagenetic Composition • Constituent Name: Illite • Habit: Fibrous and Rim • Location = Intergranular

Discontinuous Pore-Lining

Diagenetic Composition • Constituent Name: Iron

Oxide/Hydroxide and Hematite

• Habit: Coating • Location = Intergranular

Continuous Pore-Lining

threshold value that represents the minimum amount of evidence needed to indicate it, and the visual chunks in the k-graph nodes can be combined to increase the influence and the certainty of the interpretation stated.

Abel [5] has modeled 6 K-graphs, expressing 6 possible knowledge-based interpre-tations of the “diagenetic environment” in oil-reservoir rocks. For instance, the “Con-tinental Meteoric Eodiagenesis Under Dry Climate Conditions” hypothesis is the root node interpretation, requiring an interpretation certainty (a threshold) of 6. This K-graph has 7 leaf nodes (each of our K-graphs has 5 to 7 nodes) to represent the follow-ing visual chunks: “Silcrete”, “Sulphate”, “Calcrete”, “Dolocrete”, “Dolomite”, “Infil-trated Clays” and “Iron oxides/hydroxides” (e.g. Fig. 4). A K-graph inference model has the role of describing this knowledge of visual chunks, which is fundamental to ontology-based content annotation of images and related problem-solving tasks espe-cially guided by visual information.

Fig. 3 and 4 shows annotated visual chunks, where only the combination of rock features seen by a geologist in images of a rock sample can lead to a rock interpreta-tion. An AND/OR tree of visual chunks is built from the ontology-based concepts closely related to visual features. These concepts are grouped together with AND operators, forming an AND relationship. OR operators also can be used, meaning that at least one concept needs to be found in a matching process. This tree describes how ontology-based domain concepts are logically combined to form an evidence that supports an interpretation, as well as how simplified concepts can be combined to describe a more abstract visual information that must be annotated.

Fig 3. The Illite visual chunk and its set of logical relationships with geometric rock features described as ontology -based rock concepts in the intermediary nodes.

The visual chunks that were modeled in K-graphs were associated with significance indexes (here represented by weights). For example, the leaf nodes on the “Continental Meteoric Eodiagenesis Under Dry Climate Conditions” K-graph have visual chunks/weights such as: “Silcrete” (weight 6), “Sulphate” (weight 6), “Calcrete” (weight 5), and so on. An interpretation can only be reached when the combination of

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Diagenetic Composition • Constituent Name: Illite • Habit: Fibrous and Rim • Location = Intergranular

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Visual Chunk

significance indexes of observed visual chunks has reached the threshold associated with it.

Fig 4. The Iron Oxides/Hydroxides visual chunk and its set of logical relationships with geometric rock features described as ontology -based rock concepts in the intermediary nodes.

The rock interpretation approach is mainly guided by a symbolic pattern-matching process of visual chunks performed over the basic description of rock image. How-ever, additional search tactics can be used to increase the certainty of an interpretation hypothesis indicated by a K-graph and supported by the visual chunks that could be matched. The image content is described through basic concept-attribute-value triples (C-A-Vs) of an instance of a rock sample.

The reasoning can be developed either driven by hypothesis (starting with a se-lected K-graph) or by data (starting from some observed evidence). In a data-driven inference, a set of visual chunks is logically matched against the C-A-Vs mentioned in the ontology-based content annotation (e.g. an image description). The visual chunks that match are considered as “activated”. They are only activated when a minimal logical set of C-A-Vs is found in the annotated image. The activated visual chunks are used to select other K-graphs that include the same visual chunk, which in their turn are used to select new visual chunks, with the aim of confirming a hypothesis of inter-pretation. This process is repeated for as long as K-graphs and visual chunks remain to be analyzed over the entire knowledge base. A solution is indicated if some K-graph has sufficient confirmation indicated by a set of activated chunks, considering the weights and threshold values. The inferred solutions comprise the matched K-graphs and the whole set of visual chunks activated.

Using a “Continental Meteoric Eodiagenesis Under Wet Climate Conditions” K-graph as examples, the visual chunks “Kaolinization Displacive” and “Kaolinization Replacive” can be selected initially (the visual chunks with the largest weights in the 6 modeled visual chunks for this interpretation). The set of logical combinations of C-A-Vs of these visual chunks is then matched against the ontology-based content anno-tation. If one of these visual chunks is activated, the other chunks “Siderite”, “Kaolin-ite Cement”, “Matrix Kaolinization”, etc, are also selected. This process can increase

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Visual Chunk

Diagenetic Composition • Constituent Name: Iron

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Continuous Pore-Lining

the certainty about this K-graph currently taken as hypothesis. The new set of se-lected visual chunks is analyzed, and the certainty of this K-graph is thereby strength-ened. Finally, the “Continental Meteoric Eodiagenesis Under Wet Climate Conditions” solution is indicated by the confirmed K-graph and by the activated visual chunks. In another example, we can show that the image in the Fig. 2 could be inferred with two different interpretations: “Deep Burial Diagenesis (Deep Mesodiagenesis) Conditions” and “Continental Meteoric Eodiagenesis Under Dry Climate Conditions”, which can respectively be indicated by “Illite” (Fig. 3) and “Iron Oxides/Hydroxides” (Fig. 4) visual chunks.

3 A Tool for Defining with K-graphs and Visual Chunks

We are presenting an ontology-based knowledge acquisition and annotation tool mainly to describe the more abstract and interpreted visual information. This tool was developed in the context of the geology and computer science project called PetroGra-pher [14], in which was developed an intelligent database application for supporting petrographic analysis, interpretation of oil reservoir rocks, and management of relevant data using resources from both knowledge-based system technology and database technology - PetroGrapher system. The overall interpretation process is controlled by a symbolic algorithm of interpretation, which is implemented as a comp onent of the PetroGrapher system. This algorithm can run the symbolic pattern matching inference steps described in the rock interpretation method, managing the main requirements of the defined process of knowledge-based interpretation of rock images.

The tool (Fig. 5) is a graphical interface to acquire, inspect and annotate the visual knowledge, recognized by the user, mainly an expert geologist, when he describes and interprets rock sample images. A geologist can recognize both simplified and complex visual characteristics, where he can use the K-graph and visual chunk structures to imprint more semantic annotation to the image. This tool allows identifying interpreta-tions, and then connect the visual chunks that support an interpretation, decreasing the granularity until the simplified concepts of the domain ontology.

Fig 5. K-graphs and visual chunks filling the gap between the image-annotation levels.

Using this tool, the user can describe the patterns of visual information as unique objects in the domain, which we call visual chunks. These visual chunks, however, most of time do not have a name in the domain ontology, and this fact has been identi-fied and studied by several authors [9]. This tool allows to treat the semantic of the cognitive objects (e.g. the visual chunks) even thought they do not have a proposi-tional identification. Since the first information the user sees in the image, when they are trying to interpret it , is the aggregates of visual features, visual chunks are the first thing that is described using this evidences-hypothesis model. The user names and describes the visual chunks that support some interpretation, assigning them weights, which mean how significant each chunk is to confirm some interpretation. The tool automatically generates the causal relation between the nodes that represent the vis-ual chunks and the interpretation they support.

This tool makes available, for each one of the visual chunks defined by the user, the domain ontology model. The domain concepts, attributes and values can be se-lected from lists on the system interface, and forward added as the image evidences which is formed by aggregations of low-level visual concepts. When the user selects one concept and one attribute from this concept, the system retrieves the domain terminology from the oil-reservoir description ontology, displaying the list of possible values that can be selected to fill the attribute, allowing to inspect the ontology in

d) List of possible attrib-utes from the ontology, which are related to the selected concept

a) Nodes representing visual objects that are described during low-level annotation steps

b) Nodes representing abstract features that are described during high-level annotation steps

c) List of concepts from the ontology, displayed to the user during the graph configuration

order to select the term that better represents the visual characteristic he wants to annotate. At the end of the description task, the user can save all the information in a relation database system.

4 Discussion

The images that we are dealing contain a large number of visual features, and there are no control about the methods that are used to produce these digital images. The visual features in images, simplified and complex ones, can contain multiple colors, textures and shapes depending on the way they were digitalized. The features can show multiple relationships between them, where many of these visual relationships only can be characterized using special concepts of the domain. Commonly it is needed more than one image to identify some visual features, showing features at different scales, light sources and angles, allowing to annotate visual features coming from different sources. The bitmap file of the images (e.g. set of pixels) is not the input to the inference; rather, our modeling view makes use of a symbolic approach for im-age analysis, as opposed to numeric approaches oriented towards low-level image attributes. We are working on these high-level of annotation now, due to the reason that even simplified rock features are harder to automatically identify. As the basic visual features of our set of rock images is not recognized by geometric or physical processing, many of them can only be identified individually by experts who investi-gate and interpret a image in a specific context. Therefore, our knowledge-based model can be understood as a high-level approach to complement automatic and semi-automatic pattern recognition and feature extraction methods.

According to [3], an ontology captures the information that is necessary to index collections of images, where it is supposed that the annotation process is based on a template model, mainly organized as agents (passive and active agents), actions, ob-jects and settings. This work applies this notion as the basic input for the content annotation. The annotation structure of visual chunk is being introduced as a form to organize and help to develop higher ontology-based levels of semantic annotation. Our visual chunk annotation model can be used to explicitly describe the information required to develop distinct knowledge-based reasoning tasks of images, such as rock images and medical images being interpreted, for example. In addition, the annotation structure of visual chunks can be instantiated to logically connect the visual informa-tion that is described through different levels of granularity, which can still be sup-ported by the basic notion of agents-action-object-setting.

According to [1], an image annotation framework should define semantic ontology-based schemes where scientists would use to describe instances of visual features at different levels of detail, using these metadata to query image repositories. The image annotation is also based on the annotation of regions of interest, which allow fine grained data annotations instead of just whole images annotations. We believe that both ontology-based annotation and regions of interest can apply our visual chunk modeling strategy to inspect more abstract structures of visual knowledge. Specifically related to these approaches, we believe that semantic visual features can be only char-

acterized using high-level concepts from the ontology, and these concepts can only be identified and pointed by both experts and trained people. For these reason, we proposes visual chunks as a structured approach to represent symbolic patterns, al-lowing to describe findings and diagnostic features as a combination of simplified image features that are annotated using simplified concepts from the ontology. Along with concepts extracted from an ontology and associated to regions of interest in an image, the visual chunks and K-graphs are true indexing structures to investigate in the description of more semantic visual features.

According to [11], classification is concerned with establishing the correct class for an object using the object characteristics, e.g. (in geology) classification of minerals in a rock using the relationships presented in a domain ontology. According to [16], the heuristic classification problem is concerned with abstract observations, which are sometimes used in place of simplified observations to generate hypotheses. Here, too, the knowledge-based interpretation of rock images is mainly based on qualitative ab-stractions (sets of visual factors associated with each other and understood together) taken as observations, which can be used to specify interpretations. But the rock in-terpretation is not a class of some well-defined interpretation; nevertheless, we can still find solutions supported by annotated visual aspects that can produce a weaker outcome, e.g. a merely “acceptable” interpretation. According to [17], the goal of diagnosis is to find solutions that explain both the initial and any additional observa-tions. Diagnostic methods are firstly based on fault models, or models of abnormal behavior (mainly defined from fault diagnosis in technical systems). Data about the abnormal behavior guides the reasoning and search to reach the solution of the prob-lem. In contrast to that, the rock interpretation process tries to explain some observa-tions, but there are no fault models to support these interpretation processes. Fur-thermore, the observations are not monotonic data in the interpretation process, but more abstract information, here annotated as visual chunks. Finally, the assessment problem described in [11] proposes a category for a case, based on a set of domain-specific norms. The rock interpretation model can be characterized as a similar problem, because it is based on a case of ontology-based content annotation of images and interpretation norms modeled as K-graphs and visual chunks. The challenge for a rock interpretation approach comparing with assessment methods is to consider explicitly a problem of knowledge-based interpretation of images, establishing the assumptions that arise from visual knowledge modeling.

5 Conclusion

We justify the assumption that abstract types of visual knowledge are crucial to produce solutions to problems of image interpretation, as well as to develop more effective ontology-based content annotation tools, which allow to make explicit the knowledge that an image can retain. Two conceptual annotation levels are proposed, one exploring the ontology model to produce low-level semantic image annotations, and other using the visual chunk model to capture more abstract patterns of visual features. Finally, the k-graph model allow to connect these visual patterns to interpre-

tations, showing how to effectively use these information to solve tasks of knowl-edge-based interpretation of images.

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