Designing compact Gabor filter banks for efficient texture feature extraction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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