Using ga to optimize the explicitly defined skin regions for human skincolor detection
Bibliographic record
Abstract
Fast and accurate detection of human skin color is an important task in computer vision and image processing applications. Skin color detection algorithms are vital in medical application, especially in diagnosing skin diseases. This paper presents an approach for defining an explicit skin model by determining the optimal skin color regions in the selected color space. During the optimization, the skin color is defined as the union of multiple smaller regions; this is in contrast to the single region approach used in the state-of-the-art research. In this work, genetic algorithms are used to determine the boundaries of a number of skin color regions in CbCr color space to minimize the false detection rates. Using these optimized multiple regions on a challenging test dataset with uncontrolled conditions; an improvement of over 50% in the false detection is achieved compared with current CbCr based skin color detection (explicitly defined skin regions). Moreover, the proposed optimized skin model shows detection rates that are as good as Multi-layer-perceptron (MLP) and bayes classifiers with a computational cost reduction up to 80%.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".