Beyond Labels: Reclaiming Stability in Image Analysis
![A system built upon stable structural criteria [latex]S=S_{C}(X)[/latex] maintains consistent object recognition across varied conditions-including shifts in contrast, appearance, and resolution-while a semantics-first approach, predicting labels directly from input [latex]X[/latex], proves vulnerable to these same perturbations, highlighting how a revisable interpretation mapping [latex]M_{i}:S\rightarrow\mathcal{O}_{i}[/latex] allows structural validation to persist even as ontological definitions drift.](https://arxiv.org/html/2602.15712v1/x2.png)
A new approach prioritizes extracting reproducible structural information from images, independent of evolving semantic interpretations.
![A system built upon stable structural criteria [latex]S=S_{C}(X)[/latex] maintains consistent object recognition across varied conditions-including shifts in contrast, appearance, and resolution-while a semantics-first approach, predicting labels directly from input [latex]X[/latex], proves vulnerable to these same perturbations, highlighting how a revisable interpretation mapping [latex]M_{i}:S\rightarrow\mathcal{O}_{i}[/latex] allows structural validation to persist even as ontological definitions drift.](https://arxiv.org/html/2602.15712v1/x2.png)
A new approach prioritizes extracting reproducible structural information from images, independent of evolving semantic interpretations.
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