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Record W1728100752 · doi:10.1109/ccece.2003.1226107

Using color concepts in the retrieval of color artistry

2004· article· en· W1728100752 on OpenAlexaff
Jose A. Lay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage Retrieval and Classification Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceGenerative grammarRelevance (law)GrammarNatural language processingSimilarity (geometry)Artificial intelligenceMatching (statistics)Information retrievalIndex (typography)Ranking (information retrieval)LinguisticsImage (mathematics)MathematicsWorld Wide Web

Abstract

fetched live from OpenAlex

This paper presents a work on the retrieval of artworks by color artistry concepts. We argue that the query-by-example paradigm and the similarity matching engine of content-based retrieval may support only query with limited utility. We then propose a concept retrieval engine based on the generative grammar of elemental concepts methodology. In the latter, color artistry concepts are identified and used as relevance-concepts by which artworks can be collocated together. A concept language comprising elemental concepts of artistry structure and their generative grammar is devised. Elemental concepts are then represented and used to index artworks in the database. Concept queries are supported as expressions in the color artistry concept language.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.740
Threshold uncertainty score0.162

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.343
Teacher spread0.289 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2004
Admission routes1
Has abstractyes

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