State of the Art: Image Hashing
This is an incremental review that synthesizes existing methods to guide practitioners in selecting effective hashing techniques for image analysis applications.
The paper reviews state-of-the-art perceptual image hashing methods, including traditional and deep learning-based approaches, to identify the best techniques for robust feature extraction in tasks like image retrieval and duplicate detection.
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.