| Summary: | We propose original metrics for estimation of detection and identification of an object in an image. SAMI (SAliency based Metrics of Identification) gives a detection score, called D_score, and an identification score, called I_score, for a Region Of Interest (ROI), basically the footprint area of the object. The contribution of this paper is attractive since SAMI is basically a simple easy-to-implement heuristic method based on existing image processing techniques and some intuition-based postulates. SAMI has initially been conceived to estimate the performance of SCOTT, a “Synthesis COncealment Two-level Texture” algorithm. However, a direct derived application of such metrics could be the evaluation of saliency algorithms for object segmentation: the best saliency algorithm would be the one with the highest SAMI D_score for a given object. Another possible application could be the use of SAMI inside a saliency algorithm, to compute a dense modified saliency map, in which each pixel has the SAMI D_score corresponding to its neigborhood (used as ROI). Such a resulting map would be more robust to saliency noise from small spots.
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