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Record W2020828435 · doi:10.1145/2001416.2001419

Artistic line-drawings retrieval based on the pictorial content

2011· article· en· W2020828435 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal on Computing and Cultural Heritage · 2011
Typearticle
Languageen
FieldComputer Science
TopicImage Retrieval and Classification Techniques
Canadian institutionsPolytechnique Montréal
FundersUniversité Paris Descartes
KeywordsLine drawingsComputer scienceCurvatureLine (geometry)Artificial intelligenceComputer visionObserver (physics)Information retrievalComputer graphics (images)Engineering drawingMathematicsGeometry

Abstract

fetched live from OpenAlex

In this article, a general framework for the retrieval of artistic line-drawings is introduced. It relies on the pictorial content, defined as a combination of the stylistic content and the visual features of the represented subject. First, we propose an automatic method for the extraction of stroke contours in line drawings, relying on a filtering of the level lines of images. Next, the radius of the drawing tool is estimated from these segmented strokes. This information then efficiently tunes the extraction of several geometric features, including the distribution of curvature, endpoints, junctions and corners of strokes. The efficiency of the proposed method is illustrated with several experiments on two classified databases of artistic line-drawings, and compared with an approach based on the curvature scale space (CSS). Retrieval experiments suggest that the proposed framework is able to handle the pictorial effect delivered by line drawings to a human observer.

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.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.612
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.081
GPT teacher head0.273
Teacher spread0.192 · 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