Improved machine learning for image category recognition by local color constancy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Color constancy is the ability to recognize colors of objects invariant to the color of the light source. Systems for object detection or recognition in images use machine learning based on image descriptors to distinguish object and scene categories. However, there can be large variations in viewing and lighting conditions for real-world scenes, complicating the characteristics of images and consequently the image category recognition task. To reduce the effect of such variations, either color constancy algorithms or illumination-invariant color descriptors could be used. In this paper, we evaluate the performance of straightforward color constancy methods in practice, with respect to their utilization in a standard object classification problem, and also investigate their effects using local versions of these algorithms. These methods are then compared with color invariant descriptors. In a novel contribution, we ascertain that a combination of local color constancy methods and color invariant descriptors improve the performance of object recognition by as much as more than 10 percent, a significant improvement.
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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 | 0.000 | 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 | 0.000 |
| 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 it