Global semantic classification of scenes using ridgelet transform
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.
Bibliographic record
Abstract
In recent years, new harmonic analysis tools providing sparse representation in high dimension space have been proposed. In particular, ridgelets and curvelets bases are similar to the sparse components of naturally occurring image data derived empirically by computational neuroscience researchers. Ridgelets take the form of basis elements which exhibit very high directional sensitivity and are highly anisotropic. The ridgelet transform have been shown to provide a sparse representation for smooth objects with straight edges. Independently, for the purpose of scene description, the shape of the Fourier energy spectra has been used as an efficient way to provide a “holistic” description of the scene picture and its semantic category. Similarly, we focus on a simple binary semantic classification (artificial vs. natural) based on various ridgelet features. The learning stage is performed on a large image database using different state of the art Linear Discriminant techniques. Classification results are compared with those resulting from the Gabor representation. Additionally, ridgelet representation provides us with a way to accurately reconstruct the original signal. Using this synthesis step, we filter the ridgelet coefficients with the discriminant vector. The resulting image identifies the elements within the scene contributing to the different perceptual dimensions.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it