Colour logo and trademark detection in unconstrained images using colour edge gradient co-occurrence histograms
Bibliographic record
Abstract
In this paper, we present an extension of the Colour Edge Co-occurence Histogram (CECH) object detection scheme for detecting logos and trademarks in unconstrained colour images. We introduce more accurate information to the CECH by virtue of incorporating colour edge detection using vector order statistics, producing a more accurate representation of edges in images, as compared to the simple colour difference edge classification which is done in the CECH. Our proposed method is thus reliant on edge gradient information, and so we call it the Colour Edge Gradient Co-occurrence Histogram (CEGCH). We also illustrate a colour quantization scheme based in the Hue-Saturation-Value (HSV) colour space, illustrating that it is more suitable for logo and trademark detection in comparison to the colour quantization scheme used with the CECH. Results illustrate that the CEGCH detects logos and trademarks with greater accuracy in comparison to the CECH.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".