Image hashing using compressed sensing

This thesis is devoted to the analysis and implementation of image hashing based on the article "Robust image hashing with compressed sensing and ordinal measures"[3]. Image hashing uses so-called perceptual hashing methods. These methods have great applications in computer vision science, and the properties of these methods allow us to compare the similarity of hashed images and classify these images into groups. We can use this comparison, for example, to search images on the Internet for various reasons. In the theoretical part, we will talk more about the properties of these hashing methods and describe the hashing method according to the mentioned paper, we will focus most on what is compressive sampling, saliency map and how we achieve it. In the practical part, we will prepare a test dataset using Python scripting language and implement the hashing method according to the mentioned article. Then we test this hashing method on this dataset and finally compare it with another hashing method