**2. Sources for the construction of the knowledge treasure**

The field of cultural heritage research is multi- and interdisciplinary. The work of the experts in this field is quite complex, having the task of identifying and documenting as accurately and completely as possible the artifacts, to monitor their condition in order to make the most appropriate decisions regarding the interventions for the maintenance and restoration of the objects. The main issue of the cultural heritage expert is knowledge management, which is mainly based on collaborative work with specialists from complementary fields: historians, archeologists, plastic artists, ethnographers, and increasingly with specialists in transversal disciplines contributing to the investigation process: chemists, physicists, geologists, biologists, as well as computer scientists. Therefore, the major effort consists in merging information from different fields in an attempt to obtain a consolidated knowledge system regarding the heritage object. Three basic steps are distinguished in the formation of a knowledge system:


The construction of the ontology is the first stage for organizing the data and information on the path of transforming them into knowledge necessary to solve the problems of a certain domain.

**3. Automatic image analysis**

*DOI: http://dx.doi.org/10.5772/intechopen.92679*

interest [12], as follows:

• Outline descriptors

• Regional descriptors

• Texture descriptors

mosaic surfaces.

• Morphological descriptors

requirement is to get the best quality images.

**3.1 Texture descriptors**

described in **Table 2**.

**259**

the image intensity of interest [13].

Imagery is the main source of data needed to form the knowledge base of an artifact [11]. Different types of descriptors are used to characterize the outline and the interior of the interest form, topology, and morphology of the regions of

*Automation of the Expertise of the Roman Mosaic Arts in Constanta: Analytical and Statistical…*

A variety of algorithms for particular descriptors in each family mentioned above are reported in the literature. The first step is to address only those specific algorithms that contribute to the best classification of the regions of interest of the

The next step is to develop consistent knowledge based on the classification obtained. An essential step for accomplishing these steps is the integration of appropriate algorithms for the automation of the image analysis and classification process. All digital image analysis and processing algorithms are based on pixel value which depends on color, illumination, and surface quality. Therefore, information can be obtained on materials and pigments, on the degree of finishing and flatness of the mosaic pieces. The illumination of the surface of interest when acquiring the image influences globally and locally—through reflections and diffusions the value of the pixels. In principle, the image analysis is done on intensity-type images (gray level or with a single color component) and on binary images (black and withe) obtained from the first. Most descriptors, such as contour, regional, and some morphological, operate only on binary images. Thus, the results of the image analysis are strongly dependent on the level of the reference threshold used to separate the gray levels into black and white. The choice of the reference threshold is generally a compromise, its value being influenced by external factors such as ambient lighting, directional light sources, and camera position at the time of image acquisition. In these conditions it is preferable to use those methods of analysis that do not depend on the conversion threshold and operate with measures applied to the intensity images. The basic

A defining visual feature for the morphological characterization of the mosaic is considered the texture. The strongest descriptors for texture are in the category of statistics: contrast, energy, homogeneity, and entropy; they form a vector of statistical characteristics or texture attributes [12, 13]. In summary they are formally

We mention that the statistical measures of contrast, energy, and homogeneity are calculated based on the gray-level co-occurrence matrix (GLCM) derived from

The analysis of the chromatic characteristics of the mosaic can provide essential information about general and local wear, about possible restoration interventions. Chromatic analysis is applied independently of texture analysis and uses histograms

The domain ontology contributes majorly to the ordering of information by describing taxonomies, naming the categories, properties, and relationships between the specific data. Creating an ontology is a challenge that faces problems related to the reliability of information in terms of trust, incompleteness, and correctness.

Another aspect is related to the automatic generation of ontologies, which is in principle completely different from the traditional "manual" generation mode performed by knowledge engineers. Automating the generation of ontologies is also a challenge launched with Semantic Web and related technology Resource Description Framework (RDF) as a specification for data modeling. In this sense, a prominent concept is the knowledge graph used by Google, and it uses the principle of web search engine to extract relevant information and return an infobox which is a subset of structured information for the searched topic. The essential feature of this type of ontological synthesis is that it is generated ad hoc based on access to online resources such as the Wikipedia encyclopedia and the Wikidata, Wikibase, and DBpedia product suite. In this way, the actual construction of the ontology practically overlaps with the ad hoc generation of knowledge by querying large amounts of data from distributed web resources. This is the operating mechanism for virtual assistant applications such as IBM Watson, Google Assistant, Amazon Alexa, Cortana from Microsoft, Bixby from Samsung, or Apple's Siri. These products invoke artificial intelligence and understand natural language but nevertheless cannot provide expert level assistance in some areas, especially due to the lack of structured information.

The main shortcoming of ontology generation applications based on web resources predefined as online encyclopedias is the insufficient refining capacity to cover the particular issue of cultural heritage. Therefore, the constitution of the ontologies specific to the different sub-branches remains an open problem, which will be solved unequally, in time, as the expert communities will carry out concrete collaborative projects. Approaches in this area are reported in the literature [8, 9]. The collection of relevant data on heritage objects is a permanent activity through which systematic information is obtained, this being possible with advanced means of investigation using modern equipment for destructive and nondestructive analysis. The advantage of these methods is that they reveal new aspects, and a relative disadvantage of them would be the high cost of the equipment.

In the case of mosaics, as a decorative surface art, investigating the visual component is essential in obtaining nondestructive morphological and chromatic characteristics of the artifacts. Image-based investigations provide descriptions of the visual forms related to both the structural composition and the chromaticity of the areas of interest. By analyzing the image in the visible spectrum, a number of quantitative and qualitative nondestructive evaluations of the artifacts are possible [10, 11]. They can also provide valuable information and other types of passive scans, such as X-ray scanning, fluorescence, etc., and complementary physicochemical analyses of an invasive nature, which involve the taking of small samples from the mosaic.

*Automation of the Expertise of the Roman Mosaic Arts in Constanta: Analytical and Statistical… DOI: http://dx.doi.org/10.5772/intechopen.92679*
