smArt: Open and Interactive Indoor Cultural Data

smArt: Open and Interactive Indoor Cultural Data

smArt is a low-cost framework to quickly set up indoor exhibits featuring a smart navigation system for museums.

The framework is web-based and allows the design on a digital map of a sensorized museum environment and the dynamic and assisted definition of the multimedia materials and sensors associated to the artworks.

The knowledge-base uses semantic technologies and it is ex-ploited by museum visitors to get directions and to have multimedia insights in a natural way.

Indoor localisation and routing is provided taking advantage of active and passive sensors advertisements and user interactions. In this way we overcome the Global Positioning System (GPS) un-availability issue in indoor environments.

The system will be presented at ACM Multimedia 2015, Brisbane, Australia in the Demos Track.

The system started as a project developed by the students Riccardo Del Chiaro, Franco Yang, Maurizio Sanesi and Alberto Merciai as final work for the Bachelor of Art at the Faculty of Engineering of University of Florence under the supervision of prof. Alberto Del Bimbo, and his assistants Andrea Ferracani and Daniele Pezzatini.

The work was born from an idea by Andrea Ferracani and Daniele Pezzatini, researchers at MICC and prof. Alberto Del Bimbo collaborators.

PITAGORA: Recommending Users and Local Experts in anAirport Social Network

PITAGORA: Recommending Users and Local Experts in anAirport Social Network

PITAGORA is a mobile web contextual social network designed for the check-in area of an air-port. The app provides recommendation of potential friends, local experts and targeted services. Recommendation is hybrid and combines social media analysis and collaborative filtering techniques. Users’ recommendation has been evaluated through a user study with good results.

The system will be presented at ACM Multimedia 2015, Brisbane, Australia in the Demos Track.

The system started as a project developed by the students Andrea Benericetti and Marco Guiducci as final work for the Bachelor of Art at the Faculty of Engineering of University of Florence under the supervision of prof. Alberto Del Bimbo, and his assistants Andrea Ferracani and Daniele Pezzatini.

The work was born from an idea by Andrea Ferracani and Daniele Pezzatini, researchers at MICC and prof. Alberto Del Bimbo collaborators.

A System for Video Recommendation using Visual Saliency, Crowdsourced and Automatic Annotations

A System for Video Recommendation using Visual Saliency, Crowdsourced and Automatic Annotations

We’ll present at ACM Multimedia 2015 in the Demos Track a system for content-based video recommendation that exploits visual saliency to better represent video features and content.

The system is demonstrated in a Social Network we implemented. Visual saliency is used to select relevant frames to be presented in a web-based interface to tag and annotate video frames in the social network; it is also employed to summarize video content to create amore effective video representation used in the recommender system.

The system exploits automatic annotations from CNN-based classifiers on salient frames and user generated annotations.

The work was born from an idea by Andrea Ferracani and Daniele Pezzatini, researchers at MICC and prof. Alberto Del Bimbo collaborators.