Perceptual image hashing methods are often applied in various objectives, such as image retrieval, finding duplicate or near-duplicate images, and finding similar images from large-scale image content. The main challenge in image hashing techniques is robust feature extraction, which generates the same or similar hashes in images that are visually identical. In this article, we present a short review of the state-of-the-art traditional perceptual hashing and deep learning-based perceptual hashing methods, identifying the best approaches.
翻译:感知性图像散列方法常常用于各种目标,例如图像检索、寻找重复或近乎复制的图像,以及从大规模图像内容中寻找类似图像。图像散列技术的主要挑战是强力特征提取技术,在视觉相同的图像中产生相同或类似的杂质。在本篇文章中,我们简短地回顾了最先进的传统感知性散列和深层次的基于学习的感知性散列方法,确定了最佳的方法。