Texture classification using improved 2D local discriminant basis
Bibliographic record
Abstract
Local discriminant basis (LDB) is a supervised feature extraction method. Since LDB uses a redundant dictionary of wavelet packet basis, it is an excellent feature extractor for underlying features on time-frequency plane. Extraction of relevant features is a very important issue in signal and image classification. This reduces the computation complexity , while increasing the classification accuracy in the absence of adequate training samples. LDB algorithm utilizes best basis algorithm to find most discrimination basis among orthonormal redundant basis provided by wavelet packet transform. The classification performance of LDB algorithm is further improved using optimally weighted features. The genetic algorithm is utilized to find the optimal weight coefficients. The optimization step decreases within-class variance of training textures and increases between-class distances of teacher textures. The proposed algorithm is then tested on problem of composite texture segmentation. The results are compared with the weight coefficients obtained from Euclidean distance, J-divergence and Hellinger distance. Misclassification error is reduced by 26%-31%.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".