Salient region detection using feature extraction in the non‐subsampled contourlet domain
Bibliographic record
Abstract
The human visual system is attracted to the most dominant part of the image which is called salient region. There has been a surge of interest in the past few years to efficiently detect the salient regions of images. In this study, a new salient region detection method is proposed using the non‐subsampled contourlet transform. It is known that this transform is capable of providing a multiscale, multi‐directional and translation invariant decomposition of images. The proposed saliency detection method is realised by extracting various local and global features from the non‐subsampled contourlet coefficients of the colour channels. A saliency map is obtained based on a linear combination of the local features and the distribution of the global features. In order to provide a better preservation of the structure and boundary of the objects and to obtain a more uniformly highlighted salient region, the saliency map is abstracted using an optimisation framework. Several experiments are conducted on sets of natural images to evaluate the performance of the proposed method. The results show that the performance of the proposed method is superior to that of the other existing methods in terms of precision‐recall performance, F ‐measure, and mean absolute error values.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".