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Fig. 1. The top left corner coordinates and dimensions of Rols can be used for spatial annotation of movie characters. Movie scene by United Artists

Figure 1 The top left corner coordinates and dimensions of Rols can be used for spatial annotation of movie characters. Movie scene by United Artists

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Table 2. Syntax and semantics of SROTQ axioms As an example, assume a file of a video scene, namely the climax of the movie “The Good, the Bad, and the Ugly” with the trio, Tuco, Blondie, and Angel Eyes, portrayed
Table 2. Syntax and semantics of SROTQ axioms As an example, assume a file of a video scene, namely the climax of the movie “The Good, the Bad, and the Ugly” with the trio, Tuco, Blondie, and Angel Eyes, portrayed
Table 1. Syntax and semantics of SROTQ constructors Interpretation Z consists of a set A” (the domain of Z) and an interpretation function -7 which maps each atomic concept A to a set A? € A’, each atomic role R to a binary relation R* ¢ A’? x A*, and each individual name a to an element a* € A’. Similar to the constructors, the formal meaning of the axioms is defined by their model-theoretic semantics, as shown in Table 2.
Table 1. Syntax and semantics of SROTQ constructors Interpretation Z consists of a set A” (the domain of Z) and an interpretation function -7 which maps each atomic concept A to a set A? € A’, each atomic role R to a binary relation R* ¢ A’? x A*, and each individual name a to an element a* € A’. Similar to the constructors, the formal meaning of the axioms is defined by their model-theoretic semantics, as shown in Table 2.
Fig. 2. A graph visualizing the RDF triples
Fig. 2. A graph visualizing the RDF triples
Fig. 3. Vannotea, an early implementation of structured video annotation tools [33]
Fig. 3. Vannotea, an early implementation of structured video annotation tools [33]
Fig. 4. Temporal segment annotation with Advene being a proprietary Dinary format, the Native tle format OF Aavene 1S not ldeal. Nev ertheless, the software is Linked Data-ready, because every annotation, relation, an view is identified by a URI and RDF/X ML output is supported. The software does no incorporate multimedia ontologies beyond FOAF and Dublin Core though. OntoMedia™ was developed in 2006 for large multimedia collection managemen using Semantic Web technologies. The graphical user interface of this standalon Java application offered easy metadata indexing and video retrieval. OntoMedia ac cepted any input media supported by QuickTime or the Java Media Framework, an could generate RDF and relational database output. Also in 2006, Bertini and his colleagues developed the Multimedia Ontology Manag er (MOM) to combine multimedia ontology engineering with automatic annotation and generate textual and auditory commentary for video sequences [34]. The automat ic video annotation was performed for entire video clips by using similarity checkin between visual ontology concepts and extracted clips, and for video sequences b' using composite concept patterns. Video clip sequences were annotated with prede fined articulated sentences curated by the RACER reasoner. Annomation,’ published in 2008 as a collaborative Linked Data-driven narrativ hypervideo application, allowed users to semantically annotate video resources usin controlled vocabularies defined in the LOD Cloud. It was restricted to predefiner videos hosted by the service, and the semantic annotations were saved in a local re nnctnamz malzing tham inarraccihla tn avtamal camantr anantc
Fig. 4. Temporal segment annotation with Advene being a proprietary Dinary format, the Native tle format OF Aavene 1S not ldeal. Nev ertheless, the software is Linked Data-ready, because every annotation, relation, an view is identified by a URI and RDF/X ML output is supported. The software does no incorporate multimedia ontologies beyond FOAF and Dublin Core though. OntoMedia™ was developed in 2006 for large multimedia collection managemen using Semantic Web technologies. The graphical user interface of this standalon Java application offered easy metadata indexing and video retrieval. OntoMedia ac cepted any input media supported by QuickTime or the Java Media Framework, an could generate RDF and relational database output. Also in 2006, Bertini and his colleagues developed the Multimedia Ontology Manag er (MOM) to combine multimedia ontology engineering with automatic annotation and generate textual and auditory commentary for video sequences [34]. The automat ic video annotation was performed for entire video clips by using similarity checkin between visual ontology concepts and extracted clips, and for video sequences b' using composite concept patterns. Video clip sequences were annotated with prede fined articulated sentences curated by the RACER reasoner. Annomation,’ published in 2008 as a collaborative Linked Data-driven narrativ hypervideo application, allowed users to semantically annotate video resources usin controlled vocabularies defined in the LOD Cloud. It was restricted to predefiner videos hosted by the service, and the semantic annotations were saved in a local re nnctnamz malzing tham inarraccihla tn avtamal camantr anantc
Fig. 5. Comprehensive concept mapping to LOD in SemWebVid [37] SemWebVid was an Ajax web application released in 2010, which automatically generated YouTube’ video descriptions in RDF, taking manually added tags and closed captions into account. SemWebVid implemented natural language processing APIs to analyze the descriptors, and mapped the results to LOD concepts, using the DBpedia, Uberblic, Any23, and rdf:about APIs, and the now-discontinued Sindice API. Provenance data was color-coded, which was an original idea, however, the resulting text was not always easy to read (see Figure 5).
Fig. 5. Comprehensive concept mapping to LOD in SemWebVid [37] SemWebVid was an Ajax web application released in 2010, which automatically generated YouTube’ video descriptions in RDF, taking manually added tags and closed captions into account. SemWebVid implemented natural language processing APIs to analyze the descriptors, and mapped the results to LOD concepts, using the DBpedia, Uberblic, Any23, and rdf:about APIs, and the now-discontinued Sindice API. Provenance data was color-coded, which was an original idea, however, the resulting text was not always easy to read (see Figure 5).
Fig. 6. The ConnectME hypervideo annotation suite incorporated temporal information with labels and LOD concepts
Fig. 6. The ConnectME hypervideo annotation suite incorporated temporal information with labels and LOD concepts
Fig. 7. In Open Video Annotation, users can take notes on the timeline, view existing annota- tions, and play annotated video fragments individually EXMARaLDA metadata files, or SRT subtitle files, the software automatically con verts television content metadata into RDF. However, the software cannot generat RDF based on the video content alone, and is basically limited to the serialization o: existing textual data as structured data. The LinkedTV Editor’? provides a user inter: face for broadcasting services, which uses the automatically generated annotations o: LinkedTV for the rapid generation of contextual information queues. Open Video Annotation” is based on open source JavaScript libraries, such as Vid- eo.js,° Annotator, * and RangeSlider.*’ The developers claim that the software is compliant with W3C’s Open Annotation data formats. Open Video Annotation was designed to provide an intuitive interface for semantic tagging and the playback of semantically enriched videos (see Figure 7).
Fig. 7. In Open Video Annotation, users can take notes on the timeline, view existing annota- tions, and play annotated video fragments individually EXMARaLDA metadata files, or SRT subtitle files, the software automatically con verts television content metadata into RDF. However, the software cannot generat RDF based on the video content alone, and is basically limited to the serialization o: existing textual data as structured data. The LinkedTV Editor’? provides a user inter: face for broadcasting services, which uses the automatically generated annotations o: LinkedTV for the rapid generation of contextual information queues. Open Video Annotation” is based on open source JavaScript libraries, such as Vid- eo.js,° Annotator, * and RangeSlider.*’ The developers claim that the software is compliant with W3C’s Open Annotation data formats. Open Video Annotation was designed to provide an intuitive interface for semantic tagging and the playback of semantically enriched videos (see Figure 7).
Fig. 8. In MyStoryPlayer, metadata and classification are coupled with timestamp-based snapshot comments
Fig. 8. In MyStoryPlayer, metadata and classification are coupled with timestamp-based snapshot comments
Table 3. Standards supported by structured video annotation tools Open standards are preferred to proprietary implementations, such as the temporal annotation of Annomation, the spatial and temporal fragmentation of Advene and SemT ube, and proprietary ontologies, e.g., the SALERO ontologies used by IMAS or the LinkedTV ontology implemented by the LinkedTV Editor.
Table 3. Standards supported by structured video annotation tools Open standards are preferred to proprietary implementations, such as the temporal annotation of Annomation, the spatial and temporal fragmentation of Advene and SemT ube, and proprietary ontologies, e.g., the SALERO ontologies used by IMAS or the LinkedTV ontology implemented by the LinkedTV Editor.
Table 4. Supported data formats of structured video annotation tools While Linked Data output is expected from semantic video annotation tools, de- pendence on a particular LOD dataset can be a major design issue. A good example is the now-discontinued SemTube, which implemented Freebase as the primary LOD
Table 4. Supported data formats of structured video annotation tools While Linked Data output is expected from semantic video annotation tools, de- pendence on a particular LOD dataset can be a major design issue. A good example is the now-discontinued SemTube, which implemented Freebase as the primary LOD
Table 5. Ontology use of semantic video annotation tools
Table 5. Ontology use of semantic video annotation tools
Table 6. Spatiotemporal annotation support of semantic video annotation tools
Table 6. Spatiotemporal annotation support of semantic video annotation tools
Related topics:
Multimedia Indexing and SearchingVideo AnnotationMultimedia semantics
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