2023
Report
Unknown
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.euSource: ISTI Project Report, SUN, D1.1, 2023
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.euSource: ISTI Project Report, SUN, D1.1, 2023
See at: CNR ExploRA
2023
Conference article
Open Access
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.itSource: ICMR '23: International Conference on Multimedia Retrieval, pp. 649–653, Thessaloniki, Greece, 12-15/06/2023
DOI: 10.1145/3591106.3592226
Project(s): AI4Media
Metrics:
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.itSource: ICMR '23: International Conference on Multimedia Retrieval, pp. 649–653, Thessaloniki, Greece, 12-15/06/2023
DOI: 10.1145/3591106.3592226
Project(s): AI4Media
Metrics:
See at:
ISTI Repository 
| CNR ExploRA
2023
Conference article
Open Access
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.Source: MMM 2023 - 29th International Conference on Multi Media Modeling, pp. 615–621, Bergen, Norway, 9-12/01/2023
DOI: 10.1007/978-3-031-27077-2_48
Project(s): AI4Media
Metrics:
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.Source: MMM 2023 - 29th International Conference on Multi Media Modeling, pp. 615–621, Bergen, Norway, 9-12/01/2023
DOI: 10.1007/978-3-031-27077-2_48
Project(s): AI4Media
Metrics:
See at:
ISTI Repository | ZENODO | CNR ExploRA
2023
Report
Open Access
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.Source: ISTI Technical Report, ISTI-TR-2023/010, pp.1–10, 2023
DOI: 10.32079/isti-tr-2023/010
Metrics:
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.Source: ISTI Technical Report, ISTI-TR-2023/010, pp.1–10, 2023
DOI: 10.32079/isti-tr-2023/010
Metrics:
See at:
ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
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: Ital-IA 2023, pp. 538–543, Pisa, Italy, 29-31/05/2023
Project(s): AI4Media
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: Ital-IA 2023, pp. 538–543, Pisa, Italy, 29-31/05/2023
Project(s): AI4Media
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2023
Report
Open Access
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 D. A., 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.Source: ISTI Annual Reports, 2023
DOI: 10.32079/isti-ar-2023/001
Metrics:
Aloia N., Amato G., Bartalesi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., 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.Source: ISTI Annual Reports, 2023
DOI: 10.32079/isti-ar-2023/001
Metrics:
See at:
ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
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.Source: CBMI 2023 - 20th International Conference on Content-based Multimedia Indexing, pp. 158–162, Orleans, France, 20-22/09/2023
DOI: 10.1145/3617233.3617261
Project(s): AI4Media
Metrics:
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.Source: CBMI 2023 - 20th International Conference on Content-based Multimedia Indexing, pp. 158–162, Orleans, France, 20-22/09/2023
DOI: 10.1145/3617233.3617261
Project(s): AI4Media
Metrics:
See at:
ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
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.Source: MMM 2022 - 28th International Conference on Multimedia Modeling, pp. 543–548, Phu Quoc, Vietnam, 06-10/06/2022
DOI: 10.1007/978-3-030-98355-0_52
Project(s): AI4EU, AI4Media
Metrics:
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.Source: MMM 2022 - 28th International Conference on Multimedia Modeling, pp. 543–548, Phu Quoc, Vietnam, 06-10/06/2022
DOI: 10.1007/978-3-030-98355-0_52
Project(s): AI4EU
, AI4Media Metrics:
See at:
ISTI Repository | doi.org 
| link.springer.com | CNR ExploRA
2022
Other
Open Access
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
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
See at:
zenodo.org | CNR ExploRA
2022
Report
Open Access
AIMH research activities 2022
Aloia N., Amato G., Bartalesi 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 A., 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.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/002
Metrics:
Aloia N., Amato G., Bartalesi 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 A., 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.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/002
Metrics:
See at:
ISTI Repository | CNR ExploRA
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.
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
Unknown
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.
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 ExploRA | visione.isti.cnr.it
2022
Other
Unknown
SEBD 2022 Web Portal
Bolettieri P.
Web Portal of the thirtieth edition of the Italian Symposium on Advanced Database Systems SEBD 2022
Bolettieri P.
Web Portal of the thirtieth edition of the Italian Symposium on Advanced Database Systems SEBD 2022
See at: CNR ExploRA | sebd2022.isti.cnr.it
2022
Software
Unknown
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
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
See at: CNR ExploRA | visionerai.isti.cnr.it
2022
Report
Unknown
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.Source: ISTI Project Report, AI4Media, D8.2, 2022
Project(s): AI4Media
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.Source: ISTI Project Report, AI4Media, D8.2, 2022
Project(s): AI4Media
See at: CNR ExploRA
2022
Software
Unknown
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.
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 | CNR ExploRA
2022
Software
Unknown
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.
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 | CNR ExploRA
2022
Other
Unknown
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
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 ExploRA
2021
Journal article
Open Access
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
DOI: 10.48550/arxiv.2008.02749
Project(s): AI4Media
Metrics:
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
DOI: 10.48550/arxiv.2008.02749
Project(s): AI4Media
Metrics:
See at:
arXiv.org e-Print Archive | Journal of Imaging | Journal of Imaging | ISTI Repository | ISTI Repository | DOAJ-Articles | www.mdpi.com | Journal of Imaging | ZENODO | doi.org | CNR ExploRA
2021
Conference article
Open Access
VISIONE at Video Browser Showdown 2021
Amato G., Bolettieri P., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
This paper presents the second release of VISIONE, a tool for effective video search on large-scale collections. It allows users to search for videos using textual descriptions, keywords, occurrence of objects and their spatial relationships, occurrence of colors and their spatial relationships, and image similarity. One of the main features of our system is that it employs specially designed textual encodings for indexing and searching video content using the mature and scalable Apache Lucene full-text search engine.Source: MMM 2021 - 27th International Conference on Multimedia Modeling, pp. 473–478, Prague, Czech Republic, 22-24/06/2021
DOI: 10.1007/978-3-030-67835-7_47
Project(s): AI4EU, AI4Media
Metrics:
Amato G., Bolettieri P., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
This paper presents the second release of VISIONE, a tool for effective video search on large-scale collections. It allows users to search for videos using textual descriptions, keywords, occurrence of objects and their spatial relationships, occurrence of colors and their spatial relationships, and image similarity. One of the main features of our system is that it employs specially designed textual encodings for indexing and searching video content using the mature and scalable Apache Lucene full-text search engine.Source: MMM 2021 - 27th International Conference on Multimedia Modeling, pp. 473–478, Prague, Czech Republic, 22-24/06/2021
DOI: 10.1007/978-3-030-67835-7_47
Project(s): AI4EU
, AI4Media Metrics:
See at:
ISTI Repository | ZENODO | zenodo.org | Lecture Notes in Computer Science | link.springer.com | CNR ExploRA