New Distance Measurements for Image SimilarityNew Distance Measurements for Image Similarity
DOI:
https://doi.org/10.32792/jeps.v10i1.44Keywords:
image structural similarity, image similarity, Manhattan distance, Standard Deviation, Gaussian noise, Euclidian distanceAbstract
New measures have been proposed for assessing the similarity of gray-level images. The famous structuralsimilarity index measurement (SSIM) has been designed using statistical approach that fails with high
noise (lowPSNR). The two proposed measures have been suggested, the first one depend on Manhattan
distance and standard division, this measure combined from two parts: the first part depend on the
Manhattan distance which is used in geometric features the second part is based on statistical feature. The
second measure utilized the modification of Euclidian distant. The two proposed similarity measures are
outcome for human face. The new measures outperform the classical SSIM in detecting image similarity
at low PSNR, with significant difference in performance. The results were (95.3% for the first
measurement) and (99.2% for the second measurement). We used database Face94
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