E-BiT: Extended Bio-Inspired Texture Descriptor for 2D Texture Analysis and Characterization
Bibliographic record
Abstract
This paper presents an extended bio-inspired texture (E-BiT) descriptor for image texture characterization. The E-BiT descriptor combines global ecological concepts of species diversity, evenness, richness, and taxonomic indexes to effectively capture texture patterns at local and global levels while maintaining invariance to scale, translation, and permutation. First, we pre-processed the images by normalizing and applying geometric transformations to assess the invariance properties of the proposed descriptor. Next, we assessed the performance of the proposed E-BiT descriptor on four datasets, including histopathological images and natural texture images. Finally, we compared it with the original BiT descriptor and other texture descriptors, such as Haralick, GLCM, and LBP. The E-BiT descriptor achieved state-of-the-art texture classification performance, with accuracy improvements ranging from 0.12% to 20% over other descriptors. In addition, the E-BiT descriptor demonstrated its generic nature by performing well in both natural and histopathologic images. Future work could examine the E-BiT descriptor’s behavior at different spatial scales and resolutions to optimize texture property extraction and improve performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 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".