An Image Classification Method Based on Optimized Fuzzy Bag-of-words Model
Bibliographic record
Abstract
This paper proposes an image classification method based on fuzzy bag-of-words (FBoW) model and the fuzzy system with positive and negative rules. Firstly, the Gaussian membership function was adopted to construct multiple fuzzy membership histograms for image description, based on the distance between image features and multiple codebooks. Next, the fuzzy system with positive and negative rules was introduced to fuze the image description and image classification into a unified learning framework. After that, the precedent and antecedent parameters of the fuzzy system were learned by particle swarm optimization (PSO) and recursive least squares (RLS) algorithm, such that the parameters can be adjusted constantly in the learning process and that image description can fit in with the image classifier. Finally, the FBoW model was verified through experiment on the standard image dataset PASCAL Visual Object Classes Challenge 2007 (VOC2007). The results show that our method outperformed the classic FBoW model in image classification.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".