Color Image Segmentation Using Generalized Inverted Dirichlet Finite Mixture Models By Integrating Spatial Information
Bibliographic record
Abstract
Mixture models are popular statistical approaches for image segmentation. However, mixture models based segmentation faces some difficulties. The first problem is the estimation of the number of clusters (M). Secondly, the spatial information is generally not considered. In this paper, we have used two methods to counter these issues. The first method uses spatial information as the prior knowledge of M. This prior knowledge does not give the direct value of M instead it provides some indirect information which can be used to estimate the optimal value of M. The second one uses Markov Random Field (MRF) to integrate spatial information. MRF based models need high computational power due to their complexity. They cannot be used directly in the Maximization step (M-Step) of Expectation-Maximization (EM) algorithm. The MRF model used in this paper does not require high computational power and can be easily integrated with the M-Step. We have implemented Inverted Dirichlet (ID) and Generalized Inverted Dirichlet (GID) mixture models using these two methods. For experiments, we have used 500 Berkeley dataset (BSD500). In order to compare the image segmentation results, the outputs of ID mixture model (IDMM) and GID mixture model (GIDMM) are compared with the Gaussian mixture model (GMM), using segmentation performance evaluation metrics. The results obtained from GIDMM and IDMM are more promising than those obtained with GMM.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 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 | 0.001 |
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".