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2021 Journal article Open Access OPEN

The VISIONE video search system: exploiting off-the-shelf text search engines for large-scale video retrieval
Amato G., Bolettieri P., Carrara F., Debole F., Falchi F., Gennaro C., Vadicamo L., Vairo C.
This paper describes in detail VISIONE, a video search system that allows users to search for videos using textual keywords, the occurrence of objects and their spatial relationships, the occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and meet users' needs. The peculiarity of our approach is that we encode all information extracted from the keyframes, such as visual deep features, tags, color and object locations, using a convenient textual encoding that is indexed in a single text retrieval engine. This offers great flexibility when results corresponding to various parts of the query (visual, text and locations) need to be merged. In addition, we report an extensive analysis of the retrieval performance of the system, using the query logs generated during the Video Browser Showdown (VBS) 2019 competition. This allowed us to fine-tune the system by choosing the optimal parameters and strategies from those we tested.Source: JOURNAL OF IMAGING 7 (2021). doi:10.3390/jimaging7050076
DOI: 10.3390/jimaging7050076

See at: ISTI Repository Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | www.mdpi.com Open Access


2020 Report Open Access OPEN

AIMH research activities 2020
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Carrara F., Casarosa V., Ciampi L., Concordia C., Corbara S., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Thanos C., Trupiano L., Vadicamo L., Vairo C.
Annual Report of the Artificial Intelligence for Media and Humanities laboratory (AIMH) research activities in 2020.

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2019 Conference article Open Access OPEN

An Image Retrieval System for Video
Bolettieri P., Carrara F., Debole F., Falchi F., Gennaro C., Vadicamo L., Vairo C.
Since the 1970's the Content-Based Image Indexing and Retrieval (CBIR) has been an active area. Nowadays, the rapid increase of video data has paved the way to the advancement of the technologies in many different communities for the creation of Content-Based Video Indexing and Retrieval (CBVIR). However, greater attention needs to be devoted to the development of effective tools for video search and browse. In this paper, we present Visione, a system for large-scale video retrieval. The system integrates several content-based analysis and retrieval modules, including a keywords search, a spatial object-based search, and a visual similarity search. From the tests carried out by users when they needed to find as many correct examples as possible, the similarity search proved to be the most promising option. Our implementation is based on state-of-the-art deep learning approaches for content analysis and leverages highly efficient indexing techniques to ensure scalability. Specifically, we encode all the visual and textual descriptors extracted from the videos into (surrogate) textual representations that are then efficiently indexed and searched using an off-the-shelf text search engine using similarity functions.Source: International Conference on Similarity Search and Applications (SISAP), pp. 332–339, Newark, NJ, USA, 2-4/10/2019
DOI: 10.1007/978-3-030-32047-8_29

See at: ISTI Repository Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted


2019 Software Unknown

VISIONE Content-Based Video Retrieval System, VBS 2019
Amato G., Bolettieri P., Carrara F., Debole F., Falchi F., Gennaro C., Vadicamo L., Vairo C.
VISIONE is a content-based video retrieval system that participated to VBS for the very first time in 2019. It is mainly based on state-of-the-art deep learning approaches for visual content analysis and exploits highly efficient indexing techniques to ensure scalability. The system supports query by scene tag, query by object location, query by color sketch, and visual similarity search.

See at: bilioso.isti.cnr.it | CNR ExploRA


2019 Report Open Access OPEN

SmartPark@Lucca - D5. Integrazione e sperimentazione sul campo
Amato G., Bolettieri P., Carrara F., Ciampi L., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In questo deliverable sono descritte le attività eseguite all'interno del WP3, in particolare relative al Task 3.1 - Integrazione e al Task 3.2 - Sperimentazione sul campo.Source: Project report, SmartPark@Lucca, Deliverable D5, pp.1–24, 2019

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2019 Conference article Open Access OPEN

VISIONE at VBS2019
Amato G., Bolettieri P., Carrara F., Debole F., Falchi F., Gennaro C., Vadicamo L., Vairo C.
This paper presents VISIONE, a tool for large-scale video search. The tool can be used for both known-item and ad-hoc video search tasks since it integrates several content-based analysis and re- trieval modules, including a keyword search, a spatial object-based search, and a visual similarity search. Our implementation is based on state-of- the-art deep learning approaches for the content analysis and leverages highly efficient indexing techniques to ensure scalability. Specifically, we encode all the visual and textual descriptors extracted from the videos into (surrogate) textual representations that are then efficiently indexed and searched using an off-the-shelf text search engine.Source: MMM 2019 - 25th International Conference on Multimedia Modeling, pp. 591–596, Thessaloniki, Greece, 08-11/01/2019
DOI: 10.1007/978-3-030-05716-9_51

See at: ISTI Repository Open Access | link.springer.com Restricted | CNR ExploRA Restricted


2019 Software Unknown

pyfatture
Bolettieri P.
Software per la gestione delle fatture telefoniche mobile dell'istituto. Il programma analizza le fatture CSV della Convenzione Mobile 7, suddivide in costi per utenze e laboratori, evidenziando eventuali anomalie, e genera report Excel che vengono automaticamente inviati all'amministrazione dell'istituto ed ai responsabili di laboratorio. Il software è stato realizzato in Python.

See at: CNR ExploRA


2019 Software Open Access OPEN

EAGLE-IDEA Mobile App
Bolettieri P.
EAGLE-IDEA is a mobile app to visually recognize Greek and Latin inscriptions. This app is able to visually recognize approximately a million inscriptions collected by the EAGLE project. It is available on Google Play Store for Android smartphones.Project(s): EAGLE

See at: ISTI Repository Open Access | play.google.com | CNR ExploRA


2019 Conference article Open Access OPEN

Intelligenza Artificiale, Retrieval e Beni Culturali
Vadicamo L., Amato G., Bolettieri P., Falchi F., Gennaro C., Rabitti F.
La visita a musei o a luoghi di interesse di città d'arte può essere completamente reinventata attraverso modalità di fruizione moderne e dinamiche, basate su tecnologie di riconoscimento e localizzazione visuale, ricerca per immagini e visualizzazioni in realtà aumentata. Da anni il gruppo di ricerca AIMIR porta avanti attività di ricerca su queste tematiche ricoprendo anche ruoli di responsabilità in progetti nazionali ed internazionali. Questo contributo riassume alcune delle attività di ricerca svolte e delle tecnologie utilizzate, nonché la partecipazione a progetti che hanno utilizzato tecnologie di intelligenza artificiale per la valorizzazione e la fruizione del patrimonio culturale.Source: Ital-IA, Roma, 18/3/2019, 19/3/2019

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.ital-ia.it Open Access


2019 Conference article Open Access OPEN

Parking Lot Monitoring with Smart Cameras
Amato G., Bolettieri P., Carrara F., Ciampi L., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In this article, we present a scenario for monitoring the occupancy of parking spaces in the historical city of Lucca (Italy) based on the use of intelligent cameras and the most modern technologies of artificial intelligence. The system is designed to use different smart-camera prototypes: where the connection to the power grid is available, we propose a powerful embedded hardware solution that exploits a Deep Neural Network. Otherwise, a fully autonomous energy-harvesting node based on a low-energy custom board employing lightweight image analysis algorithms is considered.Source: 5th Italian Conference on ICT for Smart Cities And Communities, pp. 1–3, Pisa, Italy, 18-20 September, 2019

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2019 Report Open Access OPEN

AIMIR 2019 Research Activities
Amato G., Bolettieri P., Carrara F., Ciampi L., Di Benedetto M., Debole F., Falchi F., Gennaro C., Lagani G., Massoli F. V., Messina N., Rabitti F., Savino P., Vadicamo L., Vairo C.
Multimedia Information Retrieval (AIMIR) research group is part of the NeMIS laboratory of the Information Science and Technologies Institute "A. Faedo" (ISTI) of the Italian National Research Council (CNR). The AIMIR group has a long experience in topics related to: Artificial Intelligence, Multimedia Information Retrieval, Computer Vision and Similarity search on a large scale. We aim at investigating the use of Artificial Intelligence and Deep Learning, for Multimedia Information Retrieval, addressing both effectiveness and efficiency. Multimedia information retrieval techniques should be able to provide users with pertinent results, fast, on huge amount of multimedia data. Application areas of our research results range from cultural heritage to smart tourism, from security to smart cities, from mobile visual search to augmented reality. This report summarize the 2019 activities of the research group.Source: AIMIR Annual Report, 2019

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2018 Report Open Access OPEN

SmartPark@Lucca - D4. Progettazione e realizzazione software di riconoscimento visuale parcheggi
Amato G., Bolettieri P., Carrara F., Ciampi L., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In questo deliverable sono descritte le attività eseguite all'interno del WP2, in particolare relative al Task 2.3 - Realizzazione SW.Source: Project report, SmartPark@Lucca, Deliverable D4, pp.1–15, 2018

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2018 Conference article Open Access OPEN

A wireless smart camera network for parking monitoring
Amato G., Bolettieri P., Carrara F., Ciampi L., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In this paper we present a Wireless Sensor Network (WSN), which is intended to provide a scalable solution for active cooperative monitoring of wide geographical areas. The system is designed to use different smart-camera prototypes: where the connection to the power grid is available a powerful embedded hardware implements a Deep Neural Network, otherwise a fully autonomous energy-harvesting node based on a low-energy custom board employs lightweight image analysis algorithms. Parking lots occupancy monitoring in the historical city of Lucca (Italy) is the application where the implemented smart cameras have been deployed. Traffic monitoring and surveillance are possible new scenarios for the system.Source: 2018 IEEE Globecom Workshops (GC Workshops), pp. 1–6, Abu Dhabi, United Arab Emirates, United Arab Emirates, 9-13 December 2018
DOI: 10.1109/glocomw.2018.8644226

See at: ISTI Repository Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | xplorestaging.ieee.org Restricted


2018 Software Unknown

Art, Artworks Visual Retrieval
Amato G., Bolettieri P., Gennaro C.
A content-based image retrieval system for artworks. The system allows searching for artworks in an index of about 100.000 images, by the query by example paradigm.

See at: art.isti.cnr.it | CNR ExploRA


2017 Conference article Restricted

Social Media Image Recognition for Food Trend Analysis
Amato G., Bolettieri P., Monteiro De Lira V., Muntean C. I., Perego R., Renso C.
n increasing number of people share their thoughts and the images of their lives on social media platforms. People are exposed to food in their everyday lives and share on-line what they are eating by means of photos taken to their dishes. The hashtag #foodporn is constantly among the popular hashtags in Twitter and food photos are the second most popular subject in Instagram after selfies. The system that we propose, WorldFoodMap, captures the stream of food photos from social media and, thanks to a CNN food image classifier, identifies the categories of food that people are sharing. By collecting food images from the Twitter stream and associating food category and location to them, WorldFoodMap permits to investigate and interactively visualize the popularity and trends of the shared food all over the world.Source: SIGIR 2017 - 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1333–1336, Tokyo, Japan, 7 - 11 August, 2017
DOI: 10.1145/3077136.3084142
Project(s): SoBigData via OpenAIRE

See at: academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | dl.acm.org Restricted | dl.acm.org Restricted | CNR ExploRA Restricted


2017 Report Restricted

SmartPark@Lucca - D1. Analisi dei requisiti e specifiche hardware
Amato G., Bolettieri P., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In questo deliverable sono descritte le attività eseguite all'interno del WP1, in particolare relative ai Task 1.1 - Analisi dei Requisiti HW e Task 1.2 - Specifiche HW, che sono state svolte nei primi 3 mesi del progetto.Source: Project report, SmartPark@Lucca, Deliverable D1, pp.1–12, 2017

See at: CNR ExploRA Restricted


2017 Report Restricted

SmartPark@Lucca - D2. Analisi dei requisiti e specifiche software riconoscimento visuale parcheggi
Amato G., Bolettieri P., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In questo deliverable sono descritte le attivita? eseguite all'interno del WP2, in particolare relative ai Task 2.1 - Analisi dei Requisiti SW e Task 2.2 - Specifiche SW, che sono state svolte nei primi 3 mesi del progetto.Source: Project report, SmartPark@Lucca, Deliverable D2, pp.1–11, 2017

See at: CNR ExploRA Restricted


2017 Report Restricted

SmartPark@Lucca - D3. Progettazione e realizzazione delle telecamere intelligenti
Amato G., Bolettieri P., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In questo deliverable sono descritte le attivita? eseguite all'interno del WP1, in particolare relative al Task 1.3 - Realizzazione HW.Source: Project report, SmartPark@Lucca, Deliverable D3, pp.1–15, 2017

See at: CNR ExploRA Restricted


2017 Software Open Access OPEN

Visual food recognition
Amato G., Bolettieri P., Gennaro C.
Visual Food Recognition is a Web application that allows users to visually recognize food images. It leverages deep learning techniques based on CNNs. To perform food recognition, we used a pre-trained GoogLeNet, fine-tuned with a further training process on images from the ETHZ Food-101 dataset.

See at: ISTI Repository Open Access | food.isti.cnr.it | CNR ExploRA


2016 Journal article Restricted

Sistema di riconoscimento delle immagini e mobile app
Amato G., Bolettieri P., Falchi F., Vadicamo L.
In this paper, we present a system for visually retrieving ancient inscriptions, developed in the context of the ongoing Europeana network of Ancient Greek and Latin Epigraphy (EAGLE) EU Project. The system allows the user in front of an inscription (e.g, in a museum, street, archaeological site) or watching a reproduction (e.g., in a book, from a monitor), to automatically recognize the inscription and obtain information about it just using a smart-phone or a tablet. The experimental results show that the Vector of Locally Aggregated Descriptors is a promising encoding strategy for performing visual recognition in this specific context.Source: Forma urbis XXI (2016): 22–25.
Project(s): EAGLE

See at: CNR ExploRA Restricted