Statistical Modeling in the Wavelet Domain for Compact Feature Extraction and Similarity Measure of Images
Why this work is in the frame
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Bibliographic record
Abstract
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Image feature extraction and similarity measure in feature space are active research topics. They are basic components in a content-based image retrieval (CBIR) system. In this letter, we present a new statistical model-based image feature extraction method in the wavelet domain and a novel Kullback divergence-based similarity measure. First, a Gaussian mixture model (GMM) and a more systematic generalized Gaussian mixture model (GGMM) are employed to describe the statistical characteristics of the wavelet coefficients and the model parameters are employed to construct a compact image feature space. A nontrivial expectation-maximization (EM) algorithm for the GGMM model is derived. Subsequently, a new Kullback divergence-based similarity measure with low-computation cost is derived and analyzed. The Brodatz texture image database and some other image databases are used to evaluate the retrieval performance based on the presented new methods. Experimental results indicate that the GMM and the GGMM-based image texture features are very effective in representing multiscale image characteristics and that the new methods outperforms other conventional wavelet-based methods in retrieval performance with a comparable level of computational complexity. It is also demonstrated that for image features extracted by the new statistical models, the similarity measure based on Kullback divergence is more effective than conventional similarity measures. </para>
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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 | 0.001 | 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) | 0.000 | 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