Image segmentation by Dirichlet process mixture model with generalised mean
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Dirichlet process mixture model (DPMM) with spatial constraints – e.g. hidden Markov random field (HMRF) model – has been considered as an effective algorithm for image processing application. However, the HMRF model is complex and time‐consuming for implementation. A new DPMM has been introduced, where a generalised mean (GDM) is selected as the spatial constraints function. The GDM is applied not only on prior probability (and posterior probability) to incorporate local spatial information and component information, but also on conditional probability to incorporate local spatial information and observation information. The purpose of the HMRF model and GDM are the same for incorporating some spatial constraints into the system. However, compared to HMRF, GDM is easier, faster and simpler to implement. Finally, a variational Bayesian approach has been adopted for parameters estimation and model selection. Experimental results on image segmentation application demonstrate the improved performance of the proposed approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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