Vincenzo Moscato

TL;DR: An original approach to partitioning of a video into shots based on a foveated representation of the video using a single technique, rather than a set of dedicated methods is proposed.

TL;DR: A computational framework for human activity representation based on Petri nets is presented and the experimental results on a dataset consisting of bank surveillance videos and an unconstrained TSA tarmac surveillance dataset show that the algorithms are both fast and provide high quality results.

TL;DR: This paper proposes a novel collaborative user-centered recommendation approach in which several aspects related to users and available in Online Social Networks are considered and integrated together with items' features and context information within a general framework that can support different applications using proper customizations.

TL;DR: This work proposes and describes a novel recommending system for big data applications able to provide recommendations on the base of the interactions among users and the generated multimedia contents in one or more social media networks, relying on a “user-centered” approach.

TL;DR: This article presents a novel approach to recommendation in multimedia browsing systems, based on modeling recommendation as a social choice problem, and proposes a framework for computing customized recommendations by originally combining intrinsic features of multimedia objects, past behavior of individual users, and overall behavior of the entire community of users.