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Abstract. MPEG-7 can be used to create complex and comprehensive metadata descriptions of multimedia content. Since MPEG-7 is defined in terms of an XML schema, the semantics of its elements have no formal grounding. In addition, certain features can be described in multiple ways. In order to make MPEG-7 interoperable with domain-specific ontologies, the semantics of the MPEG-7 descriptors also need to be expressed formally in an ontology. This article describes four independent approaches to build a multimedia ontology based on the MPEG-7 standard and discusses the similarities and differences between them. 1

spatial relations. In this paper, a hybrid approach coupling ontologies and a genetic algorithm is presented for realizing knowledge-assisted semantic image analysis. The employed domain knowledge considers both high-level information referring to objects of the domain of interest and their spatial relations, and lowlevel information in terms of prototypical low-level visual descriptors. To account for the inherent in visual information ambiguity, fuzzy spatial relations have been employed and the corresponding domain ontology definitions are obtained though training. A genetic algorithm is applied to decide the most plausible annotation. Experiments with images from the beach vacation domain demonstrate the performance of the proposed approach. 1

Abstract: In this work we propose how to deal with the Semantic Gap in closed domains. That is, we propose to bridge the Semantic Gap by means of mapping wellknown low-level feature patterns found in MPEG-7 descriptions to formal concepts. The key contributions of the proposed approach are (i) the utilisation of ontologies, and rules to enhance the retrieval capabilities (effectiveness), and (ii) the realisation of the feature matching process being carried out on the structural level through indexed MPEG-7 descriptions (efficiency). We discuss advantages and shortcomings of our approach, and illustrate its application in the realm of non-linear, interactive movie productions.

In this article, an approach to semantic image analysis is presented. Under the proposed approach, ontologies are used to capture general, spatial, and contextual knowledge of a domain, and a genetic algorithm is applied to realize the final annotation. The employed domain knowledge considers high-level information in terms of the concepts of interest of the examined domain, contextual information in the form of fuzzy ontological relations, as well as low-level information in terms of prototypical low-level visual descriptors. To account for the inherent ambiguity in visual information, uncertainty has been introduced in the spatial relations definition. First, an initial hypothesis set of graded annotations is produced for each image region, and then context is exploited to update appropriately the estimated degrees of confidence. Finally, a genetic algorithm is applied to decide the most plausible annotation by utilizing the visual and the spatial concepts definitions included in the domain ontology. Experiments with a collection of photographs belonging to two different domains demonstrate the performance of the proposed approach. Keywords: context; knowledge-assisted analysis; multimedia ontologies; semantic annotation; semantic image analysis

In this paper, a learning approach coupling Support Vector Machines (SVMs) and a Genetic Algorithm (GA) is presented for knowledge-assisted semantic image analysis in specific domains. Explicitly defined domain knowledge under the proposed approach includes objects of the domain of interest and their spatial relations. SVMs are employed using low-level features to extract implicit information for each object of interest via training in order to provide an initial annotation of the image regions based solely on visual features. To account for the inherent visual information ambiguity, fuzzy spatial relations along with the previously computed initial annotations are supplied to a genetic algorithm, which decides on the globally most plausible annotation. Experiments with images of the beach vacation domain demonstrate the performance of the proposed approach. Categories and Subject Descriptors

In this paper, a complete architecture for knowledge-assisted cross-media analysis of News-related multimedia content is presented, along with its constituent components. The proposed analysis architecture employs state-of-the-art methods for the analysis of each individual modality (visual, audio, text) separately, and proposes a fusion technique based on the particular characteristics of News-related content for the combination of the individual modality analysis results. Experimental results on news broadcast video illustrate the usefulness of the proposed techniques in the automatic generation of semantic video annotations. 1.

With the advent of various services and applications of Semantic Web, semantic annotation had emerged as an important research area. The use of semantically annotated ontology had been evident in numerous information processing and retrieval tasks. One of such tasks is utilizing the semantically annotated ontology in product design which is able to suggest many important applications that are critical to aid various design related tasks. However, ontology development in design engineering remains a time consuming and tedious task that demands tremendous human efforts. In the context of product family design, management of different product information that features efficient indexing, update, navigation, search and retrieval across product families is both desirable and challenging. This paper attempts to address this issue by proposing an information management and retrieval framework based on the semantically annotated product family ontology. Particularly, we propose a document profile (DP) model to suggest semantic tags for annotation purpose. Using a case study of digital camera families, we illustrate how the faceted search and retrieval of product information can be accomplished based on the semantically annotated camera family ontology. Lastly, we briefly discuss some further research and application in design decision support, e.g. commonality and variety, based on the semantically annotated product family ontology.

Recently there has been great progress in hardware and communication technologies, which has created a large increase in the amount of multimedia information available to users. Multimedia applications become more useful as their content becomes more easily accessible, so new challenges are emerging in terms of storing, transmitting, personalizing, querying, indexing and retrieval of the multimedia content. Examples include the usage of multimedia data in business, entertainment, medicine, libraries, law and many other domains. For practical use, a description and deeper understanding of the information at the semantic level is required [5]. In this work, the exploitation of video processing and its combined use with knowledge is presented, for the extraction of a higher level understanding of the content. 1

A learning approach to knowledge-assisted image analysis and classification is proposed that combines global and local information with explicitly defined knowledge in the form of an ontology. The ontology specifies the domain of interest, its subdomains, the concepts related to each subdomain as well as contextual information. Support vector machines (SVMs) are employed in order to provide image classification to the ontology subdomains based on global image descriptions. In parallel, a segmentation algorithm is applied to segment the image into regions and SVMs are again employed, this time for performing an initial mapping between region low-level visual features and the concepts in the ontology. Then, a decision function, that receives as input the computed region-concept associations together with contextual information in the form of concept frequency of appearance, realizes image classification based on local information. A fusion mechanism subsequently combines the intermediate classification results, provided by the local- and global-level information processing, to decide on the final image classification. Once the image subdomain is selected, final region-concept association is performed using again SVMs and a genetic algorithm (GA) for optimizing the mapping between the image regions and the selected subdomain concepts taking into account contextual information in the form of spatial relations. Application of the proposed approach to images of the selected domain results in their classification (i.e., their assignment to one of the defined subdomains) and the generation of a fine granularity semantic representation of them (i.e., a segmentation map with semantic concepts attached to each segment). Experiments with images from the personal collection