A multi-strategy improved crow search algorithm for multi-level thresholding image segmentation
Bibliographic record
Abstract
The standard crow search algorithm suffers from low convergence accuracy, insufficient stability, and susceptibility to getting stuck in local optima. To tackle these formidable challenges, this paper proposes a novel multi-strategy improved crow search algorithm (MSICSA) specifically designed for multi-level image segmentation. The proposed approach incorporates three key enhancements: firstly, opposition-based learning (OBL) is utilized to improve the quality of initial solutions within MSICSA; secondly, an adaptive awareness probability mechanism is introduced to better balance the trade-off between exploration and exploitation; lastly, two differential mutation operators are developed to enhance global search capabilities, increase population diversity, and reduce the risk of converging on local optima. To validate the performance of the proposed algorithm, two sets of experiments are conducted. In the first set of experiments, CEC 2020 benchmark test functions are selected to compare the performance of MSICSA with other group intelligent optimization algorithms. In the second set of experiments, Otsu's method and fuzzy entropy are employed as objective functions for performing multilevel threshold segmentation on twelve grayscale images. The experimental results demonstrate that MSICSA outperforms seven comparison algorithms in terms of both convergence speed and segmentation quality.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".