MétaCan
Menu
Back to cohort
Record W2544232357 · doi:10.1109/icece.2006.355629

Ontology-Based Unification of MPEG-7 Semantic Descriptions

2006· article· en· W2544232357 on OpenAlexaff
Md. Anisur Rahman, M. Anwar Hossain, Iluju Kiringa, Abdulmotaleb El Saddik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Analysis and Summarization
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceOntologyInformation retrievalSemantics (computer science)UnificationKnowledge representation and reasoningAbstractionDomain (mathematical analysis)Semantic technologySemantic computingRepresentation (politics)Set (abstract data type)Description logicNatural language processingSemantic WebArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

MPEG-7 provides a rich set of tools for describing multimedia content. Among them, the semantic descriptor scheme (DS) is used to describe the semantics of the content in terms of events, objects, concepts, places, time and abstraction. However, to describe similar facts presented in different multimedia contents, different semantic descriptions may result, even after following the MPEG-7 semantic DS. These semantic descriptions may be unified in order to enhance the overall knowledge about the associated multimedia content. This paper proposed an ontology that semantically represents the structure of MPEG-7 semantic DS and acts primarily as a resource of such unification. The knowledge representation provided by this ontology can be used to develop tools that perform automatic multimedia reasoning from different existing semantic descriptions, which are narrated with other domain specific ontologies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0050.003
Science and technology studies0.0020.002
Scholarly communication0.0050.007
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.213
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicVideo Analysis and SummarizationFrench-language works237,207