Colour logo and trademark retrieval and tracking in unconstrained image sequences using Colour Edge Gradient Co-occurrence Histograms
Bibliographic record
Abstract
In this paper, we present an extension of the colour edge co-occurrence histogram (CECH) object detection scheme for retrieving and tracking colour logos and trademarks in unconstrained image sequences. We introduce more accurate information to the CECH by virtue of incorporating colour edge detection using vector order statistics, producing more accurate representations of edges in images, as compared to the simple colour difference edge classification performed in the CECH. Our proposed method is thus reliant on edge gradient information, thus naming it the colour edge gradient co-occurrence histogram (CEGCH). We also use a colour quantization scheme based in the hue-saturation-value (HSV) colour space, for it is more suitable for logo and trademark retrieval, in comparison to the colour quantization scheme used with the CECH. Results illustrate that the CEGCH retrieves and tracks 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".