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Hudelot: "Ontology Matching for the Semantic Annotation of Images
- IEEE International Conference on Fuzzy Systems, WCCI Conference
, 2010
"... Abstract-The linguistic description, i.e. semantic annotation of images can benefit from representations of useful concepts and the links between them as ontologies. Recently, several multimedia ontologies have been proposed in the literature as suitable knowledge models to bridge the well known se ..."
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Abstract-The linguistic description, i.e. semantic annotation of images can benefit from representations of useful concepts and the links between them as ontologies. Recently, several multimedia ontologies have been proposed in the literature as suitable knowledge models to bridge the well known semantic gap between low level features of image content and its high level conceptual meaning. Nevertheless, these multimedia ontologies are often dedicated to (or initially built for) particular needs or a particular application. Ontology matching, defined as the process of relating different heterogeneous models, could be a suitable approach to solve several interoperability issues that coexist in semantic image annotation and retrieval. In this paper, we propose an original and generic instance-based ontology matching approach and a methodology to extract a minimal ontology defined as the common reference between different heterogeneous ontologies. Then, this approach is applied to two dif ferent semantic image retrieval issues: the bridging of the semantic gap by the matching of a multimedia ontology with a common-sense knowledge ontology and the matching of different multimedia ontologies to extract a common reference knowledge model dedicated to several multimedia applications.
Social Computing for Collaborative Image Understanding
"... With the advance of the Internet and the increasing accessibility of computing resources, humans and computer systems are now brought together in powerful new ways. In this paper, we propose a human-centered computing framework to harness the essential characteristics of both humans and computers fo ..."
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With the advance of the Internet and the increasing accessibility of computing resources, humans and computer systems are now brought together in powerful new ways. In this paper, we propose a human-centered computing framework to harness the essential characteristics of both humans and computers for achieving collaborative image understanding (i.e., training large numbers of inter-related classifiers collaboratively for automatic object and concept detection from images), where groups of volunteers may collaborate on: (a) giving their timely guidances for supporting collaborative classifier training; (b) using their personal computing resources such as PCs for training large numbers of inter-related classifiers collaboratively; and (c) assessing the correctness of learning results (classifiers and their decision boundaries) and the effectiveness of hypotheses for classifier training.
Building Semantic Hierarchies Faithful to Image Semantics
"... Abstract. This paper proposes a new image-semantic measure, named "Semantico-Visual Relatedness of Concepts " (SVRC), to estimate the semantic similarity between concepts. The proposed measure incorpo-rates visual, conceptual and contextual information to provide a measure which is more me ..."
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Abstract. This paper proposes a new image-semantic measure, named "Semantico-Visual Relatedness of Concepts " (SVRC), to estimate the semantic similarity between concepts. The proposed measure incorpo-rates visual, conceptual and contextual information to provide a measure which is more meaningful and more representative of image semantics. We also propose a new methodology to automatically build a semantic hierarchy suitable for the purpose of image annotation and/or classifica-tion. The building is based on the previously proposed measure SVRC and on a new heuristic, named TRUST-ME, to connect concepts with higher relatedness till the building of the final hierarchy. The built hie-rarchy explicitly encodes a general to specific concepts relationship and therefore provides a semantic structure to concepts which facilitates the semantic interpretation of images. Our experiments showed that the use of the constructed semantic hierarchies as a hierarchical classification framework provides a better image annotation. 1
Enabling Interoperability between Multimedia Resources: An Ontology Matching Perspective
"... Abstract. The semantic annotation of images can benefit from repre-sentations of useful concepts and the links between them as ontologies. Recently, several multimedia ontologies have been proposed in the lit-erature as suitable knowledge models to bridge the well known seman-tic gap between low lev ..."
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Abstract. The semantic annotation of images can benefit from repre-sentations of useful concepts and the links between them as ontologies. Recently, several multimedia ontologies have been proposed in the lit-erature as suitable knowledge models to bridge the well known seman-tic gap between low level features of image content and its high level conceptual meaning. Nevertheless, these multimedia ontologies are of-ten dedicated to (or initially built for) particular needs or a particular application. Ontology matching, defined as the process of relating differ-ent heterogeneous models, we will argue, is a suitable approach to solve interoperability issues in semantic image annotation and retrieval. We propose a generic instance-based ontology matching approach, applied to an important semantic image retrieval issue: the bridging of the se-mantic gap by matching a multimedia ontology against a common-sense knowledge resource. 1
