Multifractal detrended moving average analysis for texture representation
Why this work is in the frame
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Bibliographic record
Abstract
Multifractal detrended moving average analysis (MF-DMA) is recently employed to detect long-range correlation and multifractal nature in stationary and non-stationary time series. In this paper, we propose a method to calculate the generalized Hurst exponent for each pixel of a surface based on MF-DMA, which we call the MF-DMA-based local generalized Hurst exponent. These exponents form a matrix, which we denote by LHq. These exponents are similar to the multifractal detrended fluctuation analysis (MF-DFA)-based local generalized Hurst exponent. The performance of the calculated LHq is tested for two synthetic multifractal surfaces and ten randomly chosen natural textures with analytical solutions under three cases, namely, backward (θ = 0), centered (θ = 0.5), and forward (θ = 1) with different q values and different sub-image sizes. Two sets of comparison segmentation experiments between the three cases of the MF-DMA-based LHq and the MF-DFA-based LHq show that the MF-DMA-based LHq is superior to the MF-DFA-based LHq. In addition, the backward MF-DMA algorithm is more efficient than the centered and forward algorithms. An interest finding is that the LHq with q < 0 outperforms the LHq with q > 0 in characterizing the image features of natural textures for both the MF-DMA and MF-DFA algorithms.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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