Patch-based salient region detection using statistical modeling in the non-subsampled contourlet domain
Bibliographic record
Abstract
A salient region is part of the image that captures the greatest attention by the human visual system. In this paper, we propose a novel salient region detection technique in the non-subsampled contourlet domain. The image is first decomposed into non-overlapping patches in order to fully exploit the repetitive patterns in the image. It is known that the non-subsampled contourlet transform provides an efficient multi-resolution, multi-directional, localized and shift invariant decomposition of images. In view of this, by using the statistical properties of non-subsampled contourlet coefficients of image patches, a set of feature descriptors are extracted to construct the feature map for each color channel. An entropy-based criterion is proposed to combine the channel feature maps into a saliency map. Simulations are conducted on a dataset of natural images to evaluate the performance of the proposed method and to compare it with that of the other existing methods. The results show that the proposed salient region detection method provides higher precision, recall, and F-measure and lower mean absolute error values as compared to the other existing methods.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".