Bibliographic record
Abstract
In recent years, the graph cut algorithm has been successfully applied to image segmentation because it offers numerically robust global minimum. In the graph cut framework, a parameter is often used to weight the importance of the different terms of the energy function. Usually, a fixed setting of parameters is given by the developers of the segmentation algorithm, and they are expected to give satisfactory segmentations for the images similar to those that were used to tune the parameters. But when given a different class of images, the results might not be satisfactory. In fact, there is no fixed choice of parameters that will work for all images. For each particular image, parameters must be tuned to achieve best results. The goal of this thesis is to develop a measure of segmentation quality based on different features of segmentation. Then we can run the graph cut algorithm for different values of the parameter and choose the one that gives segmentation of the highest quality. Segmentation evaluation is closely tied to the question of what constitutes a good segmentation. While evaluating segmentation results is an important task in itself, in this thesis, segmentation evaluation is a crucial task since it forms an integral part of the proposed parameter selection method. We investigate several measures of segmentation quality and our measure of segmentation quality is based on intensity, gradient, contour continuity, and texture features. We approach the problem of segmentation quality as a binary classification problem (good segmentation vs. bad segmentation), and train a classifier using the AdaBoost algorithm. AdaBoost, in addition to the class label, provides confidence estimates. A high positive value indicates that the classifier is very confident that is in the positive class (i.e. a good segmentation). Thus instead of just a binary decision, namely a good or a bad segmentation, we take the confidence value as the final measure of segmentation goodness. A new way to normalize feature weights for the AdaBoost based classifier is developed, which is particularly suitable for our framework. Our approach to feature normalization is uniquely appropriate for the parameter selection problem, and leads to a big improvement in performance. The leave- one-out cross-validation error rate is 4.4%, meaning the top quality segmentation chosen for an image is a bad segmentation in only 4.4% of cases.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".