Bounded Laplace Mixture Model with Applications to Image Clustering and Content Based Image Retrieval
Bibliographic record
Abstract
In this paper, we propose the bounded Laplace mixture model (BLMM). We also propose a new modeling scheme for wavelet coefficients based on BLMM and we apply it to image clustering and content based image retrieval (CBIR). The clustering stage is also performed by BLMM. In the proposed applications, BLMM is applied for feature extraction where each image is decomposed into a set of wavelet subspaces and a two component BLMM is adopted to illustrate the statistical characteristics of the wavelet coefficients for each wavelet subspace. The model parameters adapted from proposed model, reflect the image features of wavelet domain for each subspace and selected to formulate the feature space which is further used in clustering and CBIR. UIUC, KTH-TIPS and DTD databases are considered to demonstrate the viability and effectiveness of proposed algorithm in image clustering and CBIR. From set of experiments, BLMM has demonstrated its effectiveness in modeling the wavelet coefficients in feature extraction, image clustering and CBIR.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".