TIGGER: A Texture-Illumination Guided Global Energy Response Model for Illumination Robust Object Saliency
Bibliographic record
Abstract
Global saliency is an important aspect of many computer and robotic vision tasks, and with the increased interest infields such as autonomous navigation, a significant area of research. A challenging aspect of modelling global saliency in practical applications is the presence of varying or non-uniform illumination conditions. Many current models fail to accurately detect salient regions in non-uniform illumination conditions and often produce different saliency maps for the same image under changing illumination. In this paper, we propose a novel model for illumination robust global saliency. For a given input image, texture-illumination guided energy responses (TIGERs) are computed at different scales using a novel multi-scale extension of TIGER. To acquire these responses, image intensity is modelled as the summation of the low frequency illumination component and the high frequency texture component. A captured image is disassociated into these components via Bayesian minimization, with the required posterior probability estimated through an importance-weighted Monte Carlo sampling approach. The texture-illumination guided global energy response (TIGGER) is computed as the aggregate sum of TIGERs across all scales. The global saliency map is obtained via a k-means clustering-based region adjacency graph (RAG) model. Experimental results produce global saliency maps with improved performance in non-uniform lighting conditions and greater consistency when compared to other state-of-the-art methods.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".