Color texture retrieval usingwavelet decomposition in the independent components color space
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a color texture retrieval method using wavelet decomposition in the independent component color space. In color texture retrieval, the product of low dimensional marginal distributions of wavelet coefficients from different color layers is preferred to substitute or approximates the high dimensional joint distributions in order to avoid the curse of dimensionality. However, the RGB color spaces is a highly correlated color space and the extracted wavelet coefficients from different layers are also correlated, which means such a substitution or approximation will not be adequate. To solve the problem, we use independent component analysis to decorrelate the R, G and B layers into three new independent layers before applying wavelet decomposition on the color texture images. Experimental results show the proposed color texture retrieval method has a retrieval rate of 82.73%, while its RGB based counterpart that ignores the inter-layer correlation has a retrieval rate of 71.26%. Theoretically, our method will also have lower computataional demands than other color texture retrieval methods, which employ additional inter-layer correlation feature descriptors or hidden Markov model(HMM) in their 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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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