Logo classification using Haar wavelet co-occurrence histograms
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a system for the classification of logo and trademark images is proposed. Our proposed technique is based on using a co-occurrence histogram of the coefficients of the Haar wavelet decomposition of an image for indexing and classification. We call this histogram the wavelet co-occurrence histogram (WCH). The WCH produces a more accurate representation of the image features than does a histogram of edge direction angles in an image, since it captures the edge information and intensity variations in the image as well as the spatial separation of these features more accurately. We compare the results produced by our system to the results produced by the edge gradient histogram (EGH); a histogram of the direction angles of edges in an image. We show that when tested on a database of logos and trademarks, the retrieval results produced by our proposed system are more accurate than the EGH.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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