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2024 Conference article Open Access OPEN
Will VISIONE remain competitive in lifelog image search?
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE is a versatile video retrieval system supporting diverse search functionalities, including free-text, similarity, and temporal searches. Its recent success in securing first place in the 2024 Video Browser Showdown (VBS) highlights its effectiveness. Originally designed for analyzing, indexing, and searching diverse video content, VISIONE can also be adapted to images from lifelog cameras thanks to its reliance on frame-based representations and retrieval mechanisms. In this paper, we present an overview of VISIONE's core characteristics and the adjustments made to accommodate lifelog images. These adjustments primarily focus on enhancing result visualization within the GUI, such as grouping images by date or hour to align with lifelog dataset imagery. It's important to note that while the GUI has been updated, the core search engine and visual content analysis components remain unchanged from the version presented at VBS 2024. Specifically, metadata such as local time, GPS coordinates, and concepts associated with images are not indexed or utilized in the system. Instead, the system relies solely on the visual content of the images, with date and time information extracted from their filenames, which are utilized exclusively within the GUI for visualization purposes. Our objective is to evaluate the system's performance within the Lifelog Search Challenge, emphasizing reliance on visual content analysis without additional metadata.Project(s): AI4Media via OpenAIRE

See at: dl.acm.org Open Access | CNR IRIS Open Access | CNR IRIS Restricted


2024 Conference article Open Access OPEN
VISIONE 5.0: enhanced user interface and AI models for VBS2024
Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Fabrizio Falchi, Claudio Gennaro, Nicola Messina, Lucia Vadicamo, Claudio Vairo
In this paper, we introduce the fifth release of VISIONE, an advanced video retrieval system offering diverse search functionalities. The user can search for a target video using textual prompts, drawing objects and colors appearing in the target scenes in a canvas, or images as query examples to search for video keyframes with similar content. Compared to the previous version of our system, which was runner-up at VBS 2023, the forthcoming release, set to participate in VBS 2024, showcases a refined user interface that enhances its usability and updated AI models for more effective video content analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 14557, pp. 332-339. Amsterdam, NL, 29/01-2/02/2024
DOI: 10.1007/978-3-031-53302-0_29
Project(s): AI4Media via OpenAIRE, SUN via OpenAIRE
Metrics:


See at: CNR IRIS Open Access | doi.org Restricted | CNR IRIS Restricted | CNR IRIS Restricted


2023 Other Restricted
SUN D1.1 - Management Website
Amato G, Bolettieri P, Gennaro C, Vadicamo L, Vairo C
Report describing the online web accessible repository for all project-related documentation, which serves as the primary means for project partners to manage and share documents of the project. https://wiki.sun-xr-project.eu

See at: CNR IRIS Restricted | CNR IRIS Restricted


2023 Conference article Open Access OPEN
VISIONE: a large-scale video retrieval system with advanced search functionalities
Amato G, Bolettieri P, Carrara F, Falchi F, Gennaro C, Messina N, Vadicamo L, Vairo C
VISIONE is a large-scale video retrieval system that integrates multiple search functionalities, including free text search, spatial color and object search, visual and semantic similarity search, and temporal search. The system leverages cutting-edge AI technology for visual analysis and advanced indexing techniques to ensure scalability. As demonstrated by its runner-up position in the 2023 Video Browser Showdown competition, VISIONE effectively integrates these capabilities to provide a comprehensive video retrieval solution. A system demo is available online, showcasing its capabilities on over 2300 hours of diverse video content (V3C1+V3C2 dataset) and 12 hours of highly redundant content (Marine dataset). The demo can be accessed at https://visione.isti.cnr.itDOI: 10.1145/3591106.3592226
Project(s): AI4Media via OpenAIRE
Metrics:


See at: CNR IRIS Open Access | ISTI Repository Open Access | CNR IRIS Restricted | CNR IRIS Restricted


2023 Conference article Open Access OPEN
VISIONE at Video Browser Showdown 2023
Amato G, Bolettieri P, Carrara F, Falchi F, Gennaro C, Messina N, Vadicamo L, Vairo C
In this paper, we present the fourth release of VISIONE, a tool for fast and effective video search on a large-scale dataset. It includes several search functionalities like text search, object and color-based search, semantic and visual similarity search, and temporal search. VISIONE uses ad-hoc textual encoding for indexing and searching video content, and it exploits a full-text search engine as search backend. In this new version of the system, we introduced some changes both to the current search techniques and to the user interface.DOI: 10.1007/978-3-031-27077-2_48
Project(s): AI4Media via OpenAIRE
Metrics:


See at: CNR IRIS Open Access | ISTI Repository Open Access | ZENODO Open Access | CNR IRIS Restricted | CNR IRIS Restricted


2023 Other Open Access OPEN
CNR activity in the ESA Extension project
Vairo C, Bolettieri P, Gennaro C, Amato G
The CNR activity within the ESA "EXTENSION" project aims to develop an advanced visual recognition system for cultural heritage objects in L'Aquila, using AI techniques such as classifiers. However, this task requires substantial computational resources due to the large amount of data and deep learning-based AI techniques involved. To overcome these challenges, a centralized approach has been adopted, with a central server providing the necessary computational power and storage capacity.DOI: 10.32079/isti-tr-2023/010
Metrics:


See at: CNR IRIS Open Access | ISTI Repository Open Access | CNR IRIS Restricted


2023 Conference article Open Access OPEN
AIMH Lab 2022 activities for Vision
Ciampi L, Amato G, Bolettieri P, Carrara F, Di Benedetto M, Falchi F, Gennaro C, Messina N, Vadicamo L, Vairo C
The explosion of smartphones and cameras has led to a vast production of multimedia data. Consequently, Artificial Intelligence-based tools for automatically understanding and exploring these data have recently gained much attention. In this short paper, we report some activities of the Artificial Intelligence for Media and Humanities (AIMH) laboratory of the ISTI-CNR, tackling some challenges in the field of Computer Vision for the automatic understanding of visual data and for novel interactive tools aimed at multimedia data exploration. Specifically, we provide innovative solutions based on Deep Learning techniques carrying out typical vision tasks such as object detection and visual counting, with particular emphasis on scenarios characterized by scarcity of labeled data needed for the supervised training and on environments with limited power resources imposing miniaturization of the models. Furthermore, we describe VISIONE, our large-scale video search system designed to search extensive multimedia databases in an interactive and user-friendly manner.Source: CEUR WORKSHOP PROCEEDINGS, pp. 538-543. Pisa, Italy, 29-31/05/2023
Project(s): AI4Media via OpenAIRE, Future Artificial Intelligence Research

See at: ceur-ws.org Open Access | CNR IRIS Open Access | ISTI Repository Open Access | CNR IRIS Restricted


2023 Other Open Access OPEN
AIMH Research Activities 2023
Aloia N, Amato G, Bartalesi V, Bianchi L, Bolettieri P, Bosio C, Carraglia M, Carrara F, Casarosa V, Ciampi L, Coccomini Da, Concordia C, Corbara S, De Martino C, Di Benedetto M, Esuli A, Falchi F, Fazzari E, Gennaro C, Lagani G, Lenzi E, Meghini C, Messina N, Molinari A, Moreo A, Nardi A, Pedrotti A, Pratelli N, Puccetti G, Rabitti F, Savino P, Sebastiani F, Sperduti G, Thanos C, Trupiano L, Vadicamo L, Vairo C, Versienti L
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.DOI: 10.32079/isti-ar-2023/001
Metrics:


See at: CNR IRIS Open Access | ISTI Repository Open Access | CNR IRIS Restricted


2023 Conference article Open Access OPEN
VISIONE for newbies: an easier-to-use video retrieval system
Amato G, Bolettieri P, Carrara F, Falchi F, Gennaro C, Messina N, Vadicamo L, Vairo C
This paper presents a revised version of the VISIONE video retrieval system, which offers a wide range of search functionalities, including free text search, spatial color and object search, visual and semantic similarity search, and temporal search. The system is designed to ensure scalability using advanced indexing techniques and effectiveness using cutting-edge Artificial Intelligence technology for visual content analysis. VISIONE was the runner-up in the 2023 Video Browser Showdown competition, demonstrating its comprehensive video retrieval capabilities. In this paper, we detail the improvements made to the search and browsing interface to enhance its usability for non-expert users. A demonstration video of our system with the restyled interface, showcasing its capabilities on over 2,300 hours of diverse video content, is available online at https://youtu.be/srD3TCUkMSg.DOI: 10.1145/3617233.3617261
Project(s): AI4Media via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | CNR IRIS Restricted | CNR IRIS Restricted


2022 Conference article Open Access OPEN
VISIONE at Video Browser Showdown 2022
Amato G, Bolettieri P, Carrara F, Falchi F, Gennaro C, Messina N, Vadicamo L, Vairo C
VISIONE is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). It uses a full-text search engine as a search backend. In the latest version of our system, we modified the user interface, and we made some changes to the techniques used to analyze and search for videos.Project(s): AI4EU via OpenAIRE, AI4Media via OpenAIRE

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


2022 Other Open Access OPEN
COCO, LVIS, Open Images V4 classes mapping
Amato G, Bolettieri P, Carrara F, Falchi F, Gennaro C, Messina N, Vadicamo L, Vairo C
This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes. COCO [Lin et al 2014] contains 80 classes, LVIS [gupta2019lvis] contains 1460 classes, Open Images V4 [Kuznetsova et al. 2020] contains 601 classes. We built a mapping of these classes using a semi-automatic procedure in order to have a unique final list of 1460 classes. We also generated a hierarchy for each class, using wordnet.Project(s): AI4Media via OpenAIRE

See at: CNR IRIS Open Access | zenodo.org Open Access | CNR IRIS Restricted


2022 Other Open Access OPEN
AIMH research activities 2022
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A. D., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability.This report summarize the 2022 activities of the research group.DOI: 10.32079/isti-ar-2022/002
Metrics:


See at: CNR IRIS Open Access | ISTI Repository Open Access | CNR IRIS Restricted


2022 Software Unknown
Pyfatture v2.0
Bolettieri P.
Release 2.0 per il software per la gestione delle fatture telefoniche mobile dell'istituto. Il programma analizza le fatture CSV della Convenzione Mobile 8, 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


2022 Software Restricted
Visione IV
Amato G, Bolettieri P, Carrara F, Falchi F, Gennaro C, Messina N, Vadicamo L, Vairo C
VISIONE IV is the fourth release of a tool for fast and effective video search on a large-scale dataset. It includes several search functionalities like text search, object and color-based search, semantic and visual similarity search, and temporal search.

See at: CNR IRIS Restricted | CNR IRIS Restricted | visione.isti.cnr.it Restricted


2022 Other Restricted
SEBD 2022 Web Portal
Bolettieri P
Web Portal of the thirtieth edition of the Italian Symposium on Advanced Database Systems SEBD 2022

See at: CNR IRIS Restricted | CNR IRIS Restricted | sebd2022.isti.cnr.it Restricted


2022 Software Restricted
VisioneRAI
Amato G, Bolettieri P, Carrara F, Falchi F, Gennaro C, Messina N, Vadicamo L, Vairo C
A release of the VISIONE tool on RAI's real media content. This first prototipe was developed as part of AI4media European project , and provides a set of integrated components for browsing and searching videos by similar frames, by objects occurring in videos, by spatial relationships among objects in videos, and by cross-model search functionality (text-to-video search).Project(s): AI4Media via OpenAIRE

See at: CNR IRIS Restricted | CNR IRIS Restricted | visionerai.isti.cnr.it Restricted


2022 Other Restricted
AI4media D8.2 - Initial use case demonstrators and applications
Tzoannos S, Tsabouraki D, Konios D, Varsou E, Gray B, Dimitrov A, Dimitrov E, Kostadinov G, Gravina D, Melhart D, Henriksen L, Holmgård C, Kompatsiaris I, Papadopoulos S, Cuccovillo L, Van Kemenade P, Bocyte R, Amato G, Vadicamo L, Bolettieri P, Negro F, Montagnuolo M, Messina A, Bruccoleri A, Mignot R, Bauwens R, Overmeire L, Matton M, Garcia A
This report - accompanying the first release of the AI4Media's WP8 demonstrators - is the first one in a series of three releases. The aim of the report is to provide the reader with information about the seven AI4Media use cases, focusing on the operational environment of each use case, the features and epics covered in the first release, and the technical and non-technical activities that took place leading to the first release of the demonstrators.Project(s): AI4Media via OpenAIRE

See at: CNR IRIS Restricted | CNR IRIS Restricted


2022 Software Metadata Only Access
Extension image recognition system
Bolettieri P, Vairo C
Image recognition system realised for the ESA Extension project. The system is able to visually recognise the main monuments of the city of L'Aquila.

See at: gitea-s2i2s.isti.cnr.it Restricted | CNR IRIS Restricted


2022 Software Metadata Only Access
Image analysis and recognition system for artworks
Bolettieri P
Image analysis and recognition system realised for Cy4Gate subcontracting project. The system is able to recognise artworks in a database of millions of images.

See at: gitea-s2i2s.isti.cnr.it Restricted | CNR IRIS Restricted


2022 Other Restricted
Image analysis and recognition system for artworks, installazione e configurazione
Bolettieri P
Manuale di installazione, configurazione e uso del sistema di analisi e ricerca per immagini realizzato per il progetto conto terzi con Cy4Gate

See at: CNR IRIS Restricted | CNR IRIS Restricted