Large-Scale Concept Ontology for Multimedia

Naphade, Milind; Smith, J. R.; Tešić, Jelena; Chang, Shih-Fu; Hsu, Winston H.; Kennedy, Lyndon; Hauptmann, Alex; Curtis, Jon

doi:10.1109/mmul.2006.63

Cited by 572 publications

(383 citation statements)

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“…For example, we see possibilities to enrich lexicons of highlevel feature detectors that are used in content-based video retrieval. LSCOM is such a vocabulary for annotation and retrieval of video, containing concepts that represent realistic video retrieval problems, are observable and are (or will be) detectable with content-based video retrieval techniques [14]. In a recent effort, LSCOM was manually linked to the CyC knowledge base 6 , thus creating structure within LSCOM.…”

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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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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“…Visual attributes are semantic properties of objects (e.g., "fuzzy", "plastic") that serve as a middle ground between low-level features (e.g., color, texture) and high-level categories. Attributes (or "concepts", their counterpart in multimedia retrieval) are known to provide an effective representation for image search [15,10,20,22,4,8,25,7], especially since they permit content-based keyword queries [10,22,7]. While often treated as categorical ("is smiling" vs. "is not smiling"), attributes can more generally be modeled as continuous or relative properties ("is smiling more than X") [16,21].…”

Section: Related Workmentioning

confidence: 99%

Attribute Pivots for Guiding Relevance Feedback in Image Search

Kovashka

Grauman

2013

2013 IEEE International Conference on Computer Vision

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In interactive image search, a user iteratively refines his results by giving feedback on exemplar images. Active selection methods aim to elicit useful feedback, but traditional approaches suffer from expensive selection criteria and cannot predict informativeness reliably due to the imprecision of relevance feedback. To address these drawbacks, we propose to actively select "pivot" exemplars for which feedback in the form of a visual comparison will most reduce the system's uncertainty. For example, the system might ask, "Is your target image more or less crowded than this image?" Our approach relies on a series of binary search trees in relative attribute space, together with a selection function that predicts the information gain were the user to compare his envisioned target to the next node deeper in a given attribute's tree. It makes interactive search more efficient than existing strategies-both in terms of the system's selection time as well as the user's feedback effort.

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“…Recently published large-scale multimedia ontologies such as the Large Scale Concept Ontology for Multimedia (LSCOM) [3] as well as large annotated datasets (e.g. TRECVID, PASCAL Visual Object Classes 2 , MIRFLICKR Image Collection 3 ) have allowed an increase in multimedia concept lexicon sizes by orders of magnitude.…”

Section: Semantic Concept Detectionmentioning

confidence: 99%

Improving Automatic Video Retrieval with Semantic Concept Detection

Koskela¹,

Sjöberg²,

Laaksonen³

2009

Image Analysis

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Abstract. We study the usefulness of intermediate semantic concepts in bridging the semantic gap in automatic video retrieval. The results of a series of large-scale retrieval experiments, which combine text-based search, content-based retrieval, and concept-based retrieval, is presented. The experiments use the common video data and sets of queries from three successive TRECVID evaluations. By including concept detectors, we observe a consistent improvement on the search performance, despite the fact that the performance of the individual detectors is still often quite modest.

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Large-Scale Concept Ontology for Multimedia

Cited by 572 publications

References 0 publications

Enriching a Thesaurus to Improve Retrieval of Audiovisual Documents

Enriching a Thesaurus to Improve Retrieval of Audiovisual Documents

Attribute Pivots for Guiding Relevance Feedback in Image Search

Improving Automatic Video Retrieval with Semantic Concept Detection

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