Simple metrics to evaluate the concealment of an object: SAMI
International audience We propose original metrics for estimation of detection and identification of an object in an image: SAMI. SAMI (SAliency based Metrics of Identification) gives a detection score, called D_score, and an identification score, called I_score, for the detection evaluation and the...
Main Authors: | , , |
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Other Authors: | , , , |
Format: | Conference Object |
Language: | English |
Published: |
HAL CCSD
2014
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Subjects: | |
Online Access: | https://hal.science/hal-01116074 https://hal.science/hal-01116074/document https://hal.science/hal-01116074/file/SAMI_28042014.pdf |
Summary: | International audience We propose original metrics for estimation of detection and identification of an object in an image: SAMI. SAMI (SAliency based Metrics of Identification) gives a detection score, called D_score, and an identification score, called I_score, for the detection evaluation and the identification evaluation, respectively, for a Region Of Interest (ROI), basically the footprint area of the object. The contribution of this paper is important 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 segmentation 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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