Designing compact Gabor filter banks for efficient texture feature extraction
Bibliographic record
Abstract
Texture feature has been widely used in image segmentation, classification, retrieval and many others. Among various approaches to texture feature extraction, Gabor filtering has emerged as one of the most popular in recent years. Gabor filter-based texture feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smoothing parameters of the Gaussian envelope. In the literature, these parameters are often set by trial and error, based on the experience of the user, and the Gabor filter banks thus designed are often over-sized. To address the problem mentioned above, we propose to design compact Gabor filter banks by incorporating filter selection in this study. We develop a new Mahalanobis separability measure-based supervised approach to address the need of texture feature extraction. The strengths of our methods are twofold. Firstly, the proposed method provides a systematic way for Gabor filter bank design to avoid man-made bias. Secondly, the compact filter banks thus designed overcomes the problem of redundant or insignificant/irrelevant filter banks, and this in turn leads to improved performance of texture classification. Experimental results on benchmark datasets demonstrate the effectiveness of our proposed approach.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".