Logo and trademark detection in images using Color Wavelet Co-occurrence Histograms
Why this work is in the frame
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Bibliographic record
Abstract
The use of histograms to characterize edge information in an image is a common technique for image indexing and retrieval. Two techniques that have recently shown promising success are the edge gradient histogram (EGH) and the co-occurrence edge color histogram (CECH). In this paper, we present a system for logo and trademark retrieval from a database of color logo images. Through the use of a 5-dimensional co-occurrence histogram, the system proposed captures the co-occurrence of colors and wavelet decomposition coefficients of pairs of pixels. The result is a more precise characterization of the spatial distribution of edge information in an image than the ones produced by the EGH and the CECH. We call this 5-dimensional co-occurrence histogram the color wavelet co-occurrence histogram (CWCH). Results demonstrate that our retrieval system performs better than both the EGH and the CECH.
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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 | 0.001 |
| 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 it