Matching Graphs with Fuzzy Attributes in Machine Vision
Bibliographic record
Abstract
G.A. Bilodeau and R. Bergevin Laboratoire de vision et systemes numeriques, Pavillon Adrien-Pouliot Universite Laval, Sainte-Foy (QC), Canada, G1K 7P4 bilodeau@gel.ulaval.ca, bergevin@gel.ulaval.ca Abstract In object recognition and image querying applications, complex graphs often have to be compared to verify the similarity between two models. Since there is always uncertainty while models are constructed, the nodes and the edges require fuzzy attributes to properly describe the scene or the object. This paper addresses the problem of matching graphs with fuzzy attributes (GFAs) obtained by hypothesizing volumetric primitives from 2D parts. The GFAs of interests have nodes with many fuzzy attributes that correspond to volumetric hypotheses, and edges that describe the spatial relationship between the hypothesized volumetric primitives. A model for representing 2D parts by volumetric primitives is presented. Then, a method using structural indexing adapted to GFAs is proposed. This inexact matching method has been designed for matching GFAs in large databases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".