Convergent heterogeneous particle swarm optimisation algorithm for multilevel image thresholding segmentation
Bibliographic record
Abstract
One of the critical tasks in image processing is image segmentation. Image thresholding is the simplest technique of segmentation in two forms of bi‐level and multilevel. One alternative to find optimal threshold values is to convert the problem of segmentation into an optimisation problem. Classical optimisation techniques are computationally expensive, inaccurate and inefficient compared to the recent global heuristic optimisation algorithms. In this study, Convergence heterogeneous particle swarm optimisation (PSO) algorithm, has been utilised to find the optimal multilevel thresholds. The general idea of this algorithm is to divide particles into four subswarms for searching problem space. Otsu's and Kapur's thresholding methods are separately used as a fitness function which the former maximise between‐class variance and the latter maximise image entropy. To evaluate the proposed method, it applied to a benchmark of images and the results compared with similar and famous heuristic methods, genetic algorithm, harmony search and the PSO. The results revealed that the proposed method is accurate and robust whereas through several executions, it shows more stability with better convergence in compare to the other approaches while difference was significant by increasing the number of thresholds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".