Parallelization of a Hierarchical Graph-Based Image Segmentation using OpenMP
Bibliographic record
Abstract
In many image-processing applications, image segmentation is an essential stage. In this stage, an image is partitioned into several regions according to the similarity of its pixels. In addition to the accuracy of the image segmentation, the speed is also very important for real-time image processing applications. Many computer applications take advantages of the multi-processor architecture to up to their running performance. However, to run an algorithm as parallel is very difficult in many cases. Due to using the same memory blocks, many conflicts might be happened between the processors. Moreover, each process of one processor may depend on those of another processor. For this reason, the algorithm to be parallelized must be suitable to parallel. In addition, the processing traffic that is pursued by the processors must be controlled within some parallel directives. In this paper, we provide a parallel implementation to a hierarchical graph-based image segmentation method by using its hierarchical processing steps. To achieve this goal, we utilize the OpenMP (Open Multi-Processing) Library to run the segmentation process as parallel on images of different sizes from the INRIA Holidays dataset. The experimental results show that the parallel implementation of the algorithm is more effective than the serial type according to processing time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".