New Statistic Detector for Structural Image Similarity

Abstract

Social networks like LinkedIn, Facebook, and Instagram contribute significantly to the rise of image prevalence in daily life, with numerous images posted in everyday. Detecting image similarity is crucial for many applications. While deep learning methods like Learned Perceptual Image Patch Similarity (LPIPS) are popular, they often overlook image structure. An alternative method involves using pre-trained models ($e.g.$, LeNet-$5$ and VGG-$16$) to extract features and employing classifiers. However, deep learning methods demand substantial computational resources and they also suffer from uncontrolled false alarms. This paper proposes a novel Generalized Likelihood Ratio Test (GLRT) detector based on a hypothesis testing framework to identify the similarity of structural image pairs. The proposed approach minimizes the need for extensive computational resources, and false alarms can be regulated by employing a threshold. The detector is applied to Local Dissimilarity Maps (LDM), with gray-level values modeled by a statistical distribution. Experimental results on simulated and real data confirm its effectiveness for structural similarity detection. Additionally, a Simple Likelihood Ratio Test (SLRT) is tested on simulated data. Comparisons with deep learning and classical measures like Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) show the proposed detector performs comparably or better in terms of Area Under the Curve (AUC) with less computing time, especially for structural similarity.