Wavelet-based color texture retrieval using the independent component color space
Bibliographic record
Abstract
In this paper, we propose a wavelet based color texture retrieval method using the independent component color space. In color texture retrieval, the product of low dimensional marginal distributions of wavelet coefficients from different color layers are preferred to substitute or approximate their 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 extractedwavelet 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. In the feature extraction(FE) step of the proposed method, generalized Gaussian density (GGD) are used to model the marginal distribution of wavelet coefficients, and the extracted model parameters are used as features. In the similarity measurement (SM) step of the proposed method, the Kullback-Leibler distance(KLD) is calculated as feature distance, using the extracted model parameters of the query texture images and those of the images in the database. Experimental results on a database of 1120 color texture images indicate that the proposed method greatly overperforms its RGB based counterpart that ignores the inter-layer correlation, and its counterpart which uses the I1I2I3 colorspace.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".