Circle Views Signature: A Novel Shape Representation for Shape Recognition and Retrieval
Bibliographic record
Abstract
An important problem in computer vision is object recognition, which has received considerable attention in the literature. The performance of any object recognition system depends on the shape representation used and on the matching algorithm applied. In this paper, we propose a novel circle views (CVs) shape signature for recognizing 2-D object silhouettes. Many views from one circular orbit (or more) centered at the shape centroid are defined based on the distances from each viewing point on the circular orbit to a fixed number of sampled shape contour points. One compact and robust shape descriptor is obtained by applying the Fourier transform to the proposed signature. The obtained descriptor is translation, rotation, and scale invariant. Two popular shape benchmarks have been used for testing: 1) MPEG-7 and 2) Kimia's-99 databases. The proposed CVs signature provides a promising retrieval rate (83.71% on MPEG-7 database). A further increase in the retrieval rate (90.35%) has been achieved by applying a shape context learning technique. A slight modification to the learning technique has been proposed that reduces its computational cost significantly. An attractive feature of the proposed CVs signature is its simplicity and computational efficiency, which makes the CVs signature more practical for different application areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".