Analyzing the Revolutionary Visual Symbol System in China’s Hundred-Year Red Cartoon History Based on Computer Image Processing Technology
Bibliographic record
Abstract
Under the guidance of relevant theories and techniques, this project binarizes and segments red cartoon images, and then extracts their contour features.Neural network classifiers are used to identify and classify the outline features to realize the acquisition of visual symbols of the revolution in the history of Chinese red cartoons in the past 100 years.With the help of Pierce semiotics, the system of revolutionary visual symbols is constructed, and the system is explored in depth.Compared with other models, this paper has a high superiority on the recognition of revolutionary visual symbols in Chinese centuries-old red cartoons, and seven items of revolutionary visual symbols are extracted, specifically, flag, badge, gear, pentagram, wheat ear, hammer and sickle.In addition, the visual symbol system of the revolution has a high degree of recognition, for example, the CMYK value of the flag is 0, 100, 100, 0, and its color is red, which symbolizes the red of "passion and revolution", which well reflects the "red years" of China's development and the fruitful results of the revolution and construction.The fruit of construction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".