Semantic Annotation of Images and Videos for Multimedia Analysis

Bloehdorn, Stephan; Petridis, Kosmas; Saathoff, Carsten; Simou, Nikolaos; Tzouvaras, Vassilis; Avrithis, Yannis; Handschuh, Siegfried; Kompatsiaris, Ioannis; Staab, Steffen; Strintzis, Michael G.

doi:10.1007/11431053_40

Cited by 142 publications

(79 citation statements)

References 11 publications

(10 reference statements)

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“…MSO covers the complete set of decomposition tools from the MDS, while VDO addresses the Visual Part. The use of RDFS restricts the captured semantics to subclass and domain/range relations [5]. Both these approaches still suffer from the ambiguities that are also observed in the case of the Hunter ontology.…”

Section: Formal Representationsmentioning

confidence: 99%

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Semantic Representation of Multimedia Content

Dalakleidi

Dasiopoulou

Stoilos

et al. 2011

Knowledge-Driven Multimedia Information Extraction and Ontology Evolution

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Abstract. Multimedia documents constitute extremely rich information resources, whose efficacious management is intertwined with the effective capturing of the underlying semantics. The conveyed meaning may span along multiple levels and relates to search and retrieval tasks, much as to the very extraction and interpretation of content descriptions. In this chapter we consider the formal representation of multimedia semantics that pertain to media and domain specific descriptions, for the purpose of supporting both the extraction and subsequent semantic management of such descriptions. To this end, firstly, we present first an overview of existing approaches to the representation of multimedia content and discuss open issues. Subsequently, we present the ontology infrastructure developed in the context of the BOEMIE project tailored towards the formal representation of multimedia content. Concluding, we present what can non-standard formal representation technologies, such as fuzzy knowledge representation formalisms bring to multimedia document processing and management.

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Section: Formal Representationsmentioning

confidence: 99%

“…The ontology-based framework proposed in [5] adopts a similar perspective. A domain ontology captures the logical associations that define the relevant concepts and relations, while two MPEG-7 based ontologies model low-level visual descriptors and content structure, as described in Section 2.…”

Section: Related Workmentioning

confidence: 99%

Semantic Representation of Multimedia Content

Dalakleidi

Dasiopoulou

Stoilos

et al. 2011

Knowledge-Driven Multimedia Information Extraction and Ontology Evolution

Self Cite

View full text Add to dashboard Cite

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“…The data handled by these techniques have been rather low-level and simple: document IDs, text keywords and topic categories at most (Jeh & Widom, 2003;Micarelli & Sciarrone, 2004). The recent proposals and achievements towards the enrichment of multimedia content by formal, ontology-based, semantic descriptions open new opportunities for improvement in the personalisation field from a new, richer representational level (Bloehdorn et al, 2005;Castells et al, 2005). We see the introduction of ontology-based technology in the area of personalisation as a promising research direction (Gauch, Chaffee & Preschner, 2003).…”

Section: Ontology-based Personalisation For Content Retrievalmentioning

confidence: 99%

A Multi-Purpose Ontology-Based Approach for Personalised Content Filtering and Retrieval

Cantador

Fernández

Vallet

et al. 2008

Advances in Semantic Media Adaptation and Personalization

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Abstract. Personalised multimedia access aims at enhancing the retrieval process by complementing explicit user requests with implicit user preferences. We propose and discuss the benefits of the introduction of ontologies for an enhanced representation of the relevant knowledge about the user, the context, and the domain of discourse, as a means to enable improvements in the retrieval process and the performance of adaptive capabilities. We develop our proposal by describing techniques in several areas that exemplify the exploitation of the richness and power of formal and explicit semantics descriptions, and the improvements therein. In addition, we discuss how those explicit semantics can be learnt automatically from the analysis of the content consumed by a user, determining which concepts appear to be significant for the user's interest representation. The introduction of new preferences on the user profile should correspond to heuristics that provide a trade-off between consistency and persistence of the user's implicit interests.

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“…The semantic organization is based on the analysis and processing of text documents in the personal information space. That is, we do not consider the non-textual features of a file, although such features may facilitate data annotation [8]. A file wrapper is used to retrieve text from various types of files, such as PDF, PPT, and DOC.…”

Section: Semantic Data Organizationmentioning

confidence: 99%

A layered framework supporting personal information integration and application design for the semantic desktop

Cruz

Xiao

2008

The VLDB Journal

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Abstract. With the development of inexpensive storage devices, space usage is no longer a bottleneck for computer users. However, the increasingly large amount of personal information poses a critical problem to those users: traditional file organization in hierarchical directories may not be suited to the effective management of personal information because it ignores the semantic associations therein and bears no connection with the applications that users will run. To address such limitations, we present our vision of a semantic desktop, which relies on the use of ontologies to annotate and organize data and on the concept of personal information application (PIA), which is associated with a user's task. The PIA designer is the tool that is provided for building a variety of PIAs consisting of views (e.g., text, list, table, graph), which are spatially arranged and display interrelated fragments of the overall personal information. The semantic organization of the data follows a layered architecture that models separately the personal information, the domain data, and the application data. The network of concepts that ensues from extensive annotation and explicit associations lends itself well to rich browsing capabilities and to the formulation of expressive databaselike queries. These queries are also the basis for the interaction among views of the PIAs in the same desktop or in networked desktops. In the latter case, the concept of desktop service provides for a semantic platform for the integration of information across different desktops and the web. In this paper, we present in detail the semantic organization of the information, the overall system architecture and implementation aspects, queries and their processing, PIAs and the PIA designer, including usability studies on the designer, and the concepts of semantic navigation in a desktop and of interoperation in a network of desktops.

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Semantic Annotation of Images and Videos for Multimedia Analysis

Cited by 142 publications

References 11 publications

Semantic Representation of Multimedia Content

Semantic Representation of Multimedia Content

A Multi-Purpose Ontology-Based Approach for Personalised Content Filtering and Retrieval

A layered framework supporting personal information integration and application design for the semantic desktop

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