Superpixel-based image recognition for food images
Bibliographic record
Abstract
Food is an inseparable part of people's lives. Food image recognition has been attracting increasing attention due to the advances of Internet, imaging techniques and social media. Approaches for food image recognition are mainly focused on two main directions: low-level approaches and mid-level approaches. Low-level approaches extract low-level local features, such as SIFT or SURF, following feature encoding techniques. Mid-level approaches extract higher-level image parts and have shown promising results in many recognition problems. Compared with other image recognition problems, food images are highly deformable with large intra-class variance and small between-class variance. In this paper, considering that mid-level approaches' superior performance and superpixels segmentation methods' ability to successfully segment food parts, we propose a superpixel-based food image recognition framework to mine mid-level superpixel food parts-to-class similarity. We evaluate the proposed framework on UEC Food dataset, and show promising results when compared with existing state-of-the-art methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".