Adding Semantics to Detectors for Video Retrieval

Snoek, Cees G. M.; Huurnink, Bouke; Hollink, Laura; Rijke, Maarten de; Schreiber, Guus; Worring, Marcel

doi:10.1109/tmm.2007.900156

Cited by 174 publications

(156 citation statements)

References 49 publications

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“…The concept Weather has an unlimited amount of visual appearances, and it seems unlikely that a good coverage of these appearances can be realized in a training set to develop reliable weather detectors, which would explain the poor performance. The complementarity of semantics-based and signal-based methods was also noted in [24].…”

Section: Towards An Interdisciplinary Comparison Of Resultsmentioning

confidence: 87%

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Thesaurus enrichment for query expansion in audiovisual archives

Hollink¹,

Malaisé²,

Schreiber³

2009

Multimed Tools Appl

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It is common practice in audiovisual archives to disclose documents using metadata from a structured vocabulary or thesaurus. Many of these thesauri have limited or no structure. The objective of this paper is to find out whether retrieval of audiovisual resources from a collection indexed with an in-house thesaurus can be improved by enriching the thesaurus structure. We propose a method to add structure to a thesaurus by anchoring it to an external, semantically richer thesaurus. We investigate the added value of this enrichment for retrieval purposes. We first anchor the thesaurus to an external resource, WordNet. From this anchoring we infer relations between pairs of terms in the thesaurus that were previously unrelated. We employ the enriched thesaurus in a retrieval experiment on a TRECVID 2007 dataset. The results are promising: with simple techniques we are able to enrich a thesaurus in such a way that it adds to retrieval performance.

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Section: Towards An Interdisciplinary Comparison Of Resultsmentioning

confidence: 87%

“…In a final application this translation would be done either automatically, which is done in [24], or by the searcher, as in [10]. However, in the present paper our goal was not to build an application but to investigate the possibilities of retrieval with an automatically enriched thesaurus.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Thesaurus enrichment for query expansion in audiovisual archives

Hollink¹,

Malaisé²,

Schreiber³

2009

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…In a final application this translation would be done either automatically, which is done in [16], or by the searcher, as in [6]. However, in the present paper our goal was not to build an application but to investigate the possibilities of retrieval with an automatically enriched thesaurus.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Enriching a Thesaurus to Improve Retrieval of Audiovisual Documents

Hollink¹,

Malaisé²,

Schreiber³

2008

Semantic Multimedia

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Abstract. In many archives of audiovisual documents, retrieval is done using metadata from a structured vocabulary or thesaurus. In practice, many of these thesauri have limited or no structure. The objective of this paper is to find out whether retrieval of audiovisual resources from a collection indexed with an in-house thesaurus can be improved by anchoring the thesaurus to an external, semantically richer thesaurus. We propose a method to enrich the structure of a thesaurus and we investigate its added value for retrieval purposes. We first anchor the thesaurus to an external resource, WordNet. From this anchoring we infer relations between pairs of terms in the thesaurus that were previously unrelated. We employ the enriched thesaurus in a retrieval experiment on a TRECVid 2007 dataset. The results are promising: with simple techniques we are able to enrich a thesaurus in such a way that it adds to retrieval performance.

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“…Concept detection is used frequently in digital video retrieval to extract semantic concepts from frames of digital video footage [22] or in digital image retrieval. By matching the visual features of a frame within the footage to the properties of known 'concepts' (such as indoors, outdoors, people, crowd, etc.)…”

Section: Visual Feature Extractionmentioning

confidence: 99%