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2020
Contribution to book
Open Access
Preface - SISAP 2020
Satoh S., Vadicamo L., Zimek A., Carrara F., Bartolini I., Aumüller M., Jónsson B. Þór, Pagh R.
Preface of Volume 12440 LNCS,2020, Pages v-vi, 13th International Conference on Similarity Search and Applications, SISAP 2020.Source: Similarity Search and Applications, pp. v–vi. New York: Springer Science and Business Media, 2020
DOI: 10.1007/978-3-030-60936-8
Metrics:
Satoh S., Vadicamo L., Zimek A., Carrara F., Bartolini I., Aumüller M., Jónsson B. Þór, Pagh R.
Preface of Volume 12440 LNCS,2020, Pages v-vi, 13th International Conference on Similarity Search and Applications, SISAP 2020.Source: Similarity Search and Applications, pp. v–vi. New York: Springer Science and Business Media, 2020
DOI: 10.1007/978-3-030-60936-8
Metrics:
See at:
link.springer.com 
| ISTI Repository | link.springer.com 
| link.springer.com | CNR ExploRA
2022
Software
Unknown
NADLibrary
Cafarelli D., Vairo C., Gennaro C., Vadicamo L., Falchi F.
NADLibrary is a DLL library developed as part of the NAUSICAA project, which acts as a communication interface between the Windows application running on the ship's dashboard for remote control of the drone and the android application that controls the drone.
Cafarelli D., Vairo C., Gennaro C., Vadicamo L., Falchi F.
NADLibrary is a DLL library developed as part of the NAUSICAA project, which acts as a communication interface between the Windows application running on the ship's dashboard for remote control of the drone and the android application that controls the drone.
See at: gitea-s2i2s.isti.cnr.it | CNR ExploRA
2022
Software
Unknown
Dummy drone dashboard
Cafarelli D., Vairo C., Gennaro C., Vadicamo L., Falchi F.
Dummy drone dashboard is a C# interface, developed as part of the NAUSICAA project, that emulates the ship's dashboard to test the communication DLL developed for communication between the ship's dashboard and the android application that controls the drone.
Cafarelli D., Vairo C., Gennaro C., Vadicamo L., Falchi F.
Dummy drone dashboard is a C# interface, developed as part of the NAUSICAA project, that emulates the ship's dashboard to test the communication DLL developed for communication between the ship's dashboard and the android application that controls the drone.
See at: gitea-s2i2s.isti.cnr.it | CNR ExploRA
2020
Software
Unknown
Visione II
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE II is a content-based video retrieval system 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.
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE II is a content-based video retrieval system 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.
See at: bilioso.isti.cnr.it | 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
Software
Unknown
MOBDrone App
Cafarelli D., Vairo C., Gennaro C., Vadicamo L., Falchi F.
MOBdrone is an android app developed as part of the NAUSICAA project for automatically searching for people who have fallen overboard. It uses DJI's sdk for automatic drone flight and integrates with a DLL for interaction with the ship's dashboard and a python app for visual analysis of captured video.
Cafarelli D., Vairo C., Gennaro C., Vadicamo L., Falchi F.
MOBdrone is an android app developed as part of the NAUSICAA project for automatically searching for people who have fallen overboard. It uses DJI's sdk for automatic drone flight and integrates with a DLL for interaction with the ship's dashboard and a python app for visual analysis of captured video.
See at: gitea-s2i2s.isti.cnr.it | CNR ExploRA
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
2021
Report
Open Access
NAUSICAA - D1.2: Prototipi Analisi Visuale
Vadicamo L., Gennaro C., Cafarelli D., Falchi F.
In questo documento vengono descritte le principali attività svolte nell'ambito dell'Obiettivo Operativo n. 1 (OO1) "Progettazione dei sistemi di Intelligenza Artificiale e di Visione Artificiale per la sicurezza dell'imbarcazione" e in particolare dell'Attività A1.2 "Realizzazione prima versione prototipi Analisi Visuale".Source: ISTI Project Report, NAUSICAA, D1.2, 2021
Vadicamo L., Gennaro C., Cafarelli D., Falchi F.
In questo documento vengono descritte le principali attività svolte nell'ambito dell'Obiettivo Operativo n. 1 (OO1) "Progettazione dei sistemi di Intelligenza Artificiale e di Visione Artificiale per la sicurezza dell'imbarcazione" e in particolare dell'Attività A1.2 "Realizzazione prima versione prototipi Analisi Visuale".Source: ISTI Project Report, NAUSICAA, D1.2, 2021
See at:
ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Vec2Doc: transforming dense vectors into sparse representations for efficient information retrieval
Carrara F., Gennaro C., Vadicamo L., Amato G.
Vec2Doc: Transforming Dense Vectors into Sparse Representations for Efficient Information RetrievalSource: SISAP 2023 - 16th International Conference on Similarity Search and Applications, pp. 215–222, A Coruña, Spain, 9-11/10/2023
DOI: 10.1007/978-3-031-46994-7_18
Project(s): AI4Media
Metrics:
Carrara F., Gennaro C., Vadicamo L., Amato G.
Vec2Doc: Transforming Dense Vectors into Sparse Representations for Efficient Information RetrievalSource: SISAP 2023 - 16th International Conference on Similarity Search and Applications, pp. 215–222, A Coruña, Spain, 9-11/10/2023
DOI: 10.1007/978-3-031-46994-7_18
Project(s): AI4Media
Metrics:
See at:
ISTI Repository | CNR ExploRA
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.
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
2021
Software
Unknown
Visione III
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE III 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 this third version of our system, we modified the user interface, and we made some changes to the techniques used to analyze and search for videos.
Amato G., Bolettieri P., Carrara F., Falchi F., Gennaro C., Messina N., Vadicamo L., Vairo C.
VISIONE III 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 this third version of our system, we modified the user interface, and we made some changes to the techniques used to analyze and search for videos.
See at: bilioso.isti.cnr.it | CNR ExploRA
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
2014
Conference article
Open Access
Inscriptions visual recognition. A comparison of state-of-the-art object recognition approaches
Amato G., Falchi F., Rabitti F., Vadicamo L.
In this paper, we consider the task of recognizing inscriptions in images such as photos taken using mobile devices. Given a set of 17,155 photos related to 14,560 inscriptions, we used a ð'~-NearestNeighbor approach in order to perform the recognition. The contribution of this work is in comparing state-of-the-art visual object recognition techniques in this specific context. The experimental results conducted show that Vector of Locally Aggregated Descriptors obtained aggregating Scale Invariant Feature Transform descriptors is the best choice for this task.Source: EAGLE 2014 - First EAGLE International Conference, pp. 117–131, Parigi, Francia, 29-30 September - 1 October 2014
Project(s): EAGLE
Amato G., Falchi F., Rabitti F., Vadicamo L.
In this paper, we consider the task of recognizing inscriptions in images such as photos taken using mobile devices. Given a set of 17,155 photos related to 14,560 inscriptions, we used a ð'~-NearestNeighbor approach in order to perform the recognition. The contribution of this work is in comparing state-of-the-art visual object recognition techniques in this specific context. The experimental results conducted show that Vector of Locally Aggregated Descriptors obtained aggregating Scale Invariant Feature Transform descriptors is the best choice for this task.Source: EAGLE 2014 - First EAGLE International Conference, pp. 117–131, Parigi, Francia, 29-30 September - 1 October 2014
Project(s): EAGLE
See at:
www.eagle-network.eu | CNR ExploRA
2014
Conference article
Restricted
Aggregating local descriptors for epigraphs recognition
Amato G., Falchi F., Rabitti F., Vadicamo L.
In this paper, we consider the task of recognizing epigraphs in images such as photos taken using mobile devices. Given a set of 17,155 photos related to 14,560 epigraphs, we used a k-NearestNeighbor approach in order to perform the recognition. The contribution of this work is in evaluating state-of-the-art visual object recognition techniques in this specific context. The experimental results conducted show that Vector of Locally Aggregated Descriptors obtained aggregating SIFT descriptors is the best choice for this task.Source: The Fourth International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage, pp. 49–58, Veliko Tarnovo, Bulgaria, 18-21 September 2014
Project(s): EAGLE
Amato G., Falchi F., Rabitti F., Vadicamo L.
In this paper, we consider the task of recognizing epigraphs in images such as photos taken using mobile devices. Given a set of 17,155 photos related to 14,560 epigraphs, we used a k-NearestNeighbor approach in order to perform the recognition. The contribution of this work is in evaluating state-of-the-art visual object recognition techniques in this specific context. The experimental results conducted show that Vector of Locally Aggregated Descriptors obtained aggregating SIFT descriptors is the best choice for this task.Source: The Fourth International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage, pp. 49–58, Veliko Tarnovo, Bulgaria, 18-21 September 2014
Project(s): EAGLE
See at:
www.ceeol.com | CNR ExploRA
2015
Conference article
Open Access
Visual recognition in the EAGLE Project
Amato G., Bolettieri P., Falchi F., Rabitti F., Vadicamo L.
In this paper, we present a system for visually retrieving an- cient 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 experi- mental results show that the Vector of Locally Aggregated Descriptors is a promising encoding strategy for performing visual recognition in this specific context.Source: Italian Information Retrieval Workshop, pp. 2–4, Cagliari, Italy, 25-26/05/2015
Project(s): EAGLE
Amato G., Bolettieri P., Falchi F., Rabitti F., Vadicamo L.
In this paper, we present a system for visually retrieving an- cient 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 experi- mental results show that the Vector of Locally Aggregated Descriptors is a promising encoding strategy for performing visual recognition in this specific context.Source: Italian Information Retrieval Workshop, pp. 2–4, Cagliari, Italy, 25-26/05/2015
Project(s): EAGLE
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2016
Journal article
Unknown
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
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
2016
Conference article
Open Access
Combining Fisher Vector and Convolutional Neural Networks for image retrieval
Amato G., Falchi F., Rabitti F., Vadicamo L.
Fisher Vector (FV) and deep Convolutional Neural Network (CNN) are two popular approaches for extracting effective image representations. FV aggregates local information (e.g., SIFT) and have been state-of-the-art before the recent success of deep learning approaches. Recently, combination of FV and CNN has been investigated. However, only the aggregation of SIFT has been tested. In this work, we propose combining CNN and FV built upon binary local features, called BMM-FV. The results show that BMM-FV and CNN improve the latter retrieval performance with less computational effort with respect to the use of the traditional FV which relies on non-binary features.Source: Italian Information Retrieval Workshop, Venezia, Italy, 30-31 May 2016
Amato G., Falchi F., Rabitti F., Vadicamo L.
Fisher Vector (FV) and deep Convolutional Neural Network (CNN) are two popular approaches for extracting effective image representations. FV aggregates local information (e.g., SIFT) and have been state-of-the-art before the recent success of deep learning approaches. Recently, combination of FV and CNN has been investigated. However, only the aggregation of SIFT has been tested. In this work, we propose combining CNN and FV built upon binary local features, called BMM-FV. The results show that BMM-FV and CNN improve the latter retrieval performance with less computational effort with respect to the use of the traditional FV which relies on non-binary features.Source: Italian Information Retrieval Workshop, Venezia, Italy, 30-31 May 2016
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2017
Report
Open Access
ISTI Young Research Award 2017
Barsocchi P., Basile D., Candela L., Ciancia V., Delle Piane M., Esuli A., Ferrari A., Girardi M., Guidotti R., Lonetti F., Moroni D., Nardini F. M., Rinzivillo S., Vadicamo L.
The ISTI Young Researcher Award is an award for young people of Institute of Information Science and Technologies (ISTI) with high scientific production. In particular, the award is granted to young staff members (less than 35 years old) by assessing the yearly scientific production of the year preceding the award. This report documents procedure and results of the 2017 edition of the award.Source: ISTI Technical reports, 2017
Barsocchi P., Basile D., Candela L., Ciancia V., Delle Piane M., Esuli A., Ferrari A., Girardi M., Guidotti R., Lonetti F., Moroni D., Nardini F. M., Rinzivillo S., Vadicamo L.
The ISTI Young Researcher Award is an award for young people of Institute of Information Science and Technologies (ISTI) with high scientific production. In particular, the award is granted to young staff members (less than 35 years old) by assessing the yearly scientific production of the year preceding the award. This report documents procedure and results of the 2017 edition of the award.Source: ISTI Technical reports, 2017
See at:
ISTI Repository | CNR ExploRA
2018
Report
Open Access
SMART NEWS - Visual Content Mining
Amato G., Carrara F., Falchi F., Gennaro C., Vadicamo L.
Il deliverable D3.3 "Visual Content Mining" ha lo scopo di descrivere e documentare le attività di visual content mining portate avanti come parte dell'obiettivo operativo 3 "Social Media Analysis/Mining" del progetto "Smart News: Social Sensing for Breaking News" . In particolare, questo documento descrive lo stato dell'arte e le tecniche adottate o sviluppate in SmartNews per l'analisi automatica delle immagini al fine di estrarre informazioni che ne permettano la loro descrizione automatica, classificazione e ricerca. Tali analisi verranno integrate nel News Management tool per l'analisi delle immagini raccolte dal sistema (attività 3.1 "Data Collection") fornendo agli utenti della piattaforma degli strumenti innovativi per l'analisi dei dati e l'arricchimento delle informazioni raccolte su una notizia monitorata.Source: Project report, SMART NEWS, Deliverable D3.3, 2018
Amato G., Carrara F., Falchi F., Gennaro C., Vadicamo L.
Il deliverable D3.3 "Visual Content Mining" ha lo scopo di descrivere e documentare le attività di visual content mining portate avanti come parte dell'obiettivo operativo 3 "Social Media Analysis/Mining" del progetto "Smart News: Social Sensing for Breaking News" . In particolare, questo documento descrive lo stato dell'arte e le tecniche adottate o sviluppate in SmartNews per l'analisi automatica delle immagini al fine di estrarre informazioni che ne permettano la loro descrizione automatica, classificazione e ricerca. Tali analisi verranno integrate nel News Management tool per l'analisi delle immagini raccolte dal sistema (attività 3.1 "Data Collection") fornendo agli utenti della piattaforma degli strumenti innovativi per l'analisi dei dati e l'arricchimento delle informazioni raccolte su una notizia monitorata.Source: Project report, SMART NEWS, Deliverable D3.3, 2018
See at:
ISTI Repository | CNR ExploRA
2018
Conference article
Open Access
Selecting sketches for similarity search
Mic V., Novak D., Vadicamo L., Zezula P.
Techniques of the Hamming embedding, producing bit string sketches, have been recently successfully applied to speed up similarity search. Sketches are usually compared by the Hamming distance, and applied to filter out non-relevant objects during the query evaluation. As several sketching techniques exist and each can produce sketches with different lengths, it is hard to select a proper configuration for a particular dataset. We assume that the (dis)similarity of objects is expressed by an arbitrary metric function, and we propose a way to efficiently estimate the quality of sketches using just a small sample set of data. Our approach is based on a probabilistic analysis of sketches which describes how separated are objects after projection to the Hamming space.Source: ADBIS 2018 - 22nd European Conference on Advances in Databases and Information Systems, pp. 127–141, Budapest, Ungheria, 2-5 September 2018
DOI: 10.1007/978-3-319-98398-1_9
Metrics:
Mic V., Novak D., Vadicamo L., Zezula P.
Techniques of the Hamming embedding, producing bit string sketches, have been recently successfully applied to speed up similarity search. Sketches are usually compared by the Hamming distance, and applied to filter out non-relevant objects during the query evaluation. As several sketching techniques exist and each can produce sketches with different lengths, it is hard to select a proper configuration for a particular dataset. We assume that the (dis)similarity of objects is expressed by an arbitrary metric function, and we propose a way to efficiently estimate the quality of sketches using just a small sample set of data. Our approach is based on a probabilistic analysis of sketches which describes how separated are objects after projection to the Hamming space.Source: ADBIS 2018 - 22nd European Conference on Advances in Databases and Information Systems, pp. 127–141, Budapest, Ungheria, 2-5 September 2018
DOI: 10.1007/978-3-319-98398-1_9
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA