3D content stored in big databases or shared on the Internet is a precious resource for several applications, but unfortunately it risks being underexploited due to the difficulty of retrieving it efficiently In this paper we describe a system called the ''ShapeAnnotator'' through which it is possible to perform non-trivial segmentations of 3D surface meshes and annotate the detected parts through concepts expressed by an ontology Each part is connected to an instance that can be stored in a knowledge base to ease the retrieval process based on semantics Through an intuitive interface, users create such instances by simply selecting proper classes in the ontology; attributes and relations with other instances can be computed automatically based on a customizable analysis of the underlying topology and geometry of the parts We show how our part-based annotation framework can be used in two scenarios, namely for the creation of avatars in emerging Internet-based virtual worlds, and for product design in e-manufacturing

http://saturno.ge.imati.cnr.it/ima/personal/robbiano/src/JCAD09-preprint.pdf

Characterization of 3D shape parts for semantic annotation

The availability of semantically annotated image and video assets constitutes a critical prerequisite for the realisation of intelligent knowledge management services pertaining to realistic user needs. Given the extend of the challenges involved in the automatic extraction of such descriptions, manually created metadata play a significant role, further strengthened by their deployment in training and evaluation tasks related to the automatic extraction of content descriptions. The different views taken by the two main approaches towards semantic content description, namely the Semantic Web and MPEG-7, as well as the traits particular to multimedia content due to the multiplicity of information levels involved, have resulted in a variety of image and video annotation tools, adopting varying description aspects. Aiming to provide a common framework of reference and furthermore to highlight open issues, especially with respect to the coverage and the interoperability of the produced metadata, in this chapter we present an overview of the state of the art in image and video annotation tools.

/pdf/a-survey-of-semantic-image-and-video-annotation-tools-10gkrpgxvq.pdf

A survey of semantic image and video annotation tools

Despite significant progress over the last years the large-scale adoption of semantic technologies is still to come. One of the reasons for this state of affairs is assumed to be the lack of useful semantic content, a prerequisite for almost every IT system or application using semantics. Through its very nature, this content can not be created fully automatically, but requires, to a certain degree, human contribution. The interest of Internet users in semantics, and in particular in creating semantic content, is, however, low. This is understandable if we think of several characteristics exposed by many of the most prominent semantic technologies, and the applications thereof. One of these characteristics is the high barrier of entry imposed. Interacting with semantic technologies today requires specific skills and expertise on subjects which are not part of the mainstream IT knowledge portfolio. A second characteristic are the incentives that are largely missing in the design of most semantic applications. The benefits of using machine-understandable content are in most applications fully decoupled from the effort of creating and maintaining this content. In other words, users do not have a motivation to contribute to the process. Initiatives in the areas of the Social Semantic Web acknowledged this problem, and identified mechanisms to motivate users to dedicate more of their time and resources to participate in the semantic content creation process. Still, even if incentives are theoretically in place, available human labor is limited and must only be used for those tasks that are heavily dependent on human intervention, and cannot be reliably automated. In this article, we concentrate on this step in between. As a first contribution, we analyze the process of semantic content creation in order to identify those tasks that are inherently human-driven. When building semantic applications involving these specific tasks, one has to install incentive schemes that are likely to encourage users to perform exactly these tasks that crucially rely on manual input. As a second contribution of the article, we propose incentives or incentive-driven tools that can be used to increase user interest in semantic content creation tasks. We hope that our findings will be adopted as recommendations for establishing a fundamentally new form of design of semantic applications by the semantic technologies community.

/pdf/human-intelligence-in-the-process-of-semantic-content-34hjsrzd7x.pdf

Human Intelligence in the Process of Semantic Content Creation

Early prototype multimedia database management systems used the query-by-example paradigm to respond to user queries. Users needed to formulate their queries by providing examples or sketches. The query-by-keyword paradigm, on the other hand, has emerged due to the desire to search multimedia content in terms of semantic concepts using keywords or sentences rather than low-level multimedia descriptors. After all, it's much easier to formulate some queries by using keywords. However, some queries are still easier to formulate by examples or sketches-for example, the trajectory of a moving object.

/pdf/bilvideo-7-an-mpeg-7-compatible-video-indexing-and-retrieval-11rcbyeqmq.pdf

Bilvideo-7: an MPEG-7- compatible video indexing and retrieval system

http://www.cs.bilkent.edu.tr/~bilmdg/bilvideo-7/bilvideo7-mm2010.pdf

Annotations of multimedia documents typically have been pursued in two different directions. Either previous approaches have focused on low level descriptors, such as dominant color, or they have focused on the content dimension and corresponding annotations, such as person or vehicle. In this paper, we present a software environment to bridge between the two directions. M-OntoMat-Annotizer allows for linking low level MPEG-7 visual descriptions to conventional Semantic Web ontologies and annotations. We use M-OntoMat-Annotizer in order to construct ontologies that include prototypical instances of high-level domain concepts together with a formal specification of corresponding visual descriptors. Thus, we formalize the interrelationship of high- and low-level multimedia concept descriptions allowing for new kinds of multimedia content analysis, reasoning and retrieval.

M-OntoMat-Annotizer: image annotation linking ontologies and multimedia low-level features

https://mklab.iti.gr/files/pdf/eswc06.pdf

Norman Timmermann

Papers

M-OntoMat-Annotizer: image annotation linking ontologies and multimedia low-level features

M-OntoMat-Annotizer: Linking Ontologies with Multimedia Low-Level Features for Automatic Image Annotation