Rotation Invariant Local Frequency Descriptors for Texture Classification
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a novel rotation invariant method for texture classification based on local frequency components. The local frequency components are computed by applying 1-D Fourier transform on a neighboring function defined on a circle of radius R at each pixel. We observed that the low frequency components are the major constituents of the circular functions and can effectively represent textures. Three sets of features are extracted from the low frequency components, two based on the phase and one based on the magnitude. The proposed features are invariant to rotation and linear changes of illumination. Moreover, by using low frequency components, the proposed features are very robust to noise. While the proposed method uses a relatively small number of features, it outperforms state-of-the-art methods in three well-known datasets: Brodatz, Outex, and CUReT. In addition, the proposed method is very robust to noise and can remarkably improve the classification accuracy especially in the presence of high levels of noise.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 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