Structure-Guided Region-of-Interest Extraction Without Semantic Priors

This work presents an unsupervised method for region-of-interest (ROI) extraction based on local structural organization, without semantic priors, annotations, or task-specific models. ROI selection is reformulated as a problem of spatial structure and local structural density rather than object recognition or perceptual saliency.

The approach relies on dense structural maps derived from a local structural measure, which are spatially aggregated to extract compact and coherent regions of interest. The method is fully unsupervised, task-agnostic, and applicable to scenarios where scene content or downstream tasks are unknown.

Experimental results on natural and aerial image datasets show that the proposed approach achieves strong spatial reduction while preserving structurally coherent regions, outperforming classical gradient-based operators and exhibiting performance comparable to entropy-based methods.

This work provides a generic, structure-guided building block for ROI selection in computer vision pipelines, particularly suited to real-time, embedded, and multi-task applications.