Selecting a Diverse Set of Aesthetically-pleasing and Representative Video Thumbnails using Reinforcement Learning

This paper presents a new reinforcement-based method for video thumbnail selection (called RL-DiVTS), that relies on estimates of the aesthetic quality, representativeness and visual diversity of a small set of selected frames, made with the help of tailored reward functions. The proposed method integrates a novel diversity-aware Frame Picking mechanism that performs a sequential frame selection and applies a reweighting process to demote frames that are visually-similar to the already selected ones. Experiments on two benchmark datasets (OVP and YouTube), using the top-3 matching evaluation protocol, show the competitiveness of RL-DiVTS against other SoA video thumbnail selection and summarization approaches from the literature.