Determinantal Point Processes for Image Processing

Determinantal point processes (DPPs) are probabilistic models of configurations that favor diversity or repulsion. They have recently gained influence in the machine learning community, mainly because of their ability to elegantly and efficiently subsample large sets of data. In this paper, we consider DPPs from an image processing perspective, meaning that the data we want to subsample are pixels or patches of a given image. To this end, our framework is discrete and finite. First, we adapt their basic definition and properties to DPPs defined on the pixels of an image, that we call determinantal pixel processes (DPixPs). We are mainly interested in the repulsion properties of such a process and we apply DPixPs to texture synthesis using shot noise models. Finally, we study DPPs on the set of patches of an image. Because of their repulsive property, DPPs provide a strong tool to subsample discrete distributions such as that of image patches.

Automated summary

Determinantal point processes (DPPs) are probabilistic models that favor diversity or repulsion, gaining influence in the machine learning community for their elegant and efficient subsampling capabilities. This paper explores DPPs from an image processing perspective, adapting them for use in sampling pixels or patches of images, known as determinantal pixel processes (DPixPs), to study repulsion properties and apply them to texture synthesis using shot noise models. Additionally, DPPs are also studied for subsampling discrete distributions such as image patches due to their repulsive property.