2024
Other
Open Access
AIMH Research Activities 2024
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Cassese M., Ciampi L., Coccomini D. A., Concordia C., Connor R., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Iannello L., Negi K., Lagani G., Lenzi E., Leocata M., Malvaldi M., Meghini C., Messina N., Moreo Fernandez A., Nardi A., Pacini G., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Scotti F., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L., Volpi L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is committed to advancing the field of Artificial Intelligence, with a special emphasis on its applications in digital media and the humanities. The lab aims to improve AI technologies, particularly in areas such as deep learning, text analysis, computer vision, multimedia information retrieval, content analysis, recognition, and retrieval. This report summarizes the laboratory’s achievements and activities over the course of 2024.DOI: 10.32079/isti-ar-2024/001
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Cassese M., Ciampi L., Coccomini D. A., Concordia C., Connor R., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Iannello L., Negi K., Lagani G., Lenzi E., Leocata M., Malvaldi M., Meghini C., Messina N., Moreo Fernandez A., Nardi A., Pacini G., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Scotti F., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L., Volpi L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is committed to advancing the field of Artificial Intelligence, with a special emphasis on its applications in digital media and the humanities. The lab aims to improve AI technologies, particularly in areas such as deep learning, text analysis, computer vision, multimedia information retrieval, content analysis, recognition, and retrieval. This report summarizes the laboratory’s achievements and activities over the course of 2024.DOI: 10.32079/isti-ar-2024/001
Metrics:
2024
Conference article
Open Access
Tasting the time: how M-tree broke the “27 Club” curse
Ciaccia P., Patella M., Rabitti F., Zezula P.
The M-tree turned 27 this year, since it was first published in 1997, at SEBD [5] and VLDB [6]. Differently from the likes of Jim Morrison, Jimi Hendrix, and Amy Winehouse, it is still alive and kicking, receiving dozens of downloads and citations every year. In this paper, we offer a quick overview of the context in which the M-tree was created and show how it helped starting a whole family of metric trees.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3741, pp. 6-8. Villasimius, Italy, 23-26/06/2024
Ciaccia P., Patella M., Rabitti F., Zezula P.
The M-tree turned 27 this year, since it was first published in 1997, at SEBD [5] and VLDB [6]. Differently from the likes of Jim Morrison, Jimi Hendrix, and Amy Winehouse, it is still alive and kicking, receiving dozens of downloads and citations every year. In this paper, we offer a quick overview of the context in which the M-tree was created and show how it helped starting a whole family of metric trees.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3741, pp. 6-8. Villasimius, Italy, 23-26/06/2024
See at:
ceur-ws.org 
| CNR IRIS | CNR IRIS 
2023
Other
Open Access
AIMH Research Activities 2023
Aloia N., Amato G., Bartalesi Lenzi 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 Fernandez 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:
Aloia N., Amato G., Bartalesi Lenzi 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 Fernandez 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 | ISTI Repository | CNR IRIS
2022
Other
Open Access
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:
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 | ISTI Repository | CNR IRIS
2021
Other
Open Access
AIMH research activities 2021
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez 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 2021 activities of the research group.DOI: 10.32079/isti-ar-2021/003
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez 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 2021 activities of the research group.DOI: 10.32079/isti-ar-2021/003
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2020
Conference article
Open Access
Edge-Based Video Surveillance with Embedded Devices
Kavalionak H, Gennaro C, Amato G, Vairo C, Perciante C, Meghini C, Falchi F, Rabitti F
Video surveillance systems have become indispensable tools for the security and organization of public and private areas. In this work, we propose a novel distributed protocol for an edge-based face recogni-tion system that takes advantage of the computational capabilities of the surveillance devices (i.e., cameras) to perform person recognition. The cameras fall back to a centralized server if their hardware capabili-ties are not enough to perform the recognition. We evaluate the proposed algorithm via extensive experiments on a freely available dataset. As a prototype of surveillance embedded devices, we have considered a Rasp-berry PI with the camera module. Using simulations, we show that our algorithm can reduce up to 50% of the load of the server with no negative impact on the quality of the surveillance service.Source: CEUR WORKSHOP PROCEEDINGS, pp. 278-285. Villasimius, Sardinia, Italy, 21-24/06/2020
Kavalionak H, Gennaro C, Amato G, Vairo C, Perciante C, Meghini C, Falchi F, Rabitti F
Video surveillance systems have become indispensable tools for the security and organization of public and private areas. In this work, we propose a novel distributed protocol for an edge-based face recogni-tion system that takes advantage of the computational capabilities of the surveillance devices (i.e., cameras) to perform person recognition. The cameras fall back to a centralized server if their hardware capabili-ties are not enough to perform the recognition. We evaluate the proposed algorithm via extensive experiments on a freely available dataset. As a prototype of surveillance embedded devices, we have considered a Rasp-berry PI with the camera module. Using simulations, we show that our algorithm can reduce up to 50% of the load of the server with no negative impact on the quality of the surveillance service.Source: CEUR WORKSHOP PROCEEDINGS, pp. 278-285. Villasimius, Sardinia, Italy, 21-24/06/2020
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2020
Conference article
Open Access
Scalar Quantization-Based Text Encoding for Large Scale Image Retrieval
Amato G, Carrara F, Falchi F, Gennaro C, Rabitti F, Vadicamo L
The great success of visual features learned from deep neu-ral networks has led to a significant effort to develop efficient and scal- A ble technologies for image retrieval. This paper presents an approach to transform neural network features into text codes suitable for being indexed by a standard full-text retrieval engine such as Elasticsearch. The basic idea is providing a transformation of neural network features with the twofold aim of promoting the sparsity without the need of un-supervised pre-training. We validate our approach on a recent convolu-tional neural network feature, namely Regional Maximum Activations of Convolutions (R-MAC), which is a state-of-art descriptor for image retrieval. An extensive experimental evaluation conducted on standard benchmarks shows the effectiveness and efficiency of the proposed ap-proach and how it compares to state-of-the-art main-memory indexes.Source: CEUR WORKSHOP PROCEEDINGS, pp. 258-265. Virtual (online) due COVID-19, 21-24/06/2020
Amato G, Carrara F, Falchi F, Gennaro C, Rabitti F, Vadicamo L
The great success of visual features learned from deep neu-ral networks has led to a significant effort to develop efficient and scal- A ble technologies for image retrieval. This paper presents an approach to transform neural network features into text codes suitable for being indexed by a standard full-text retrieval engine such as Elasticsearch. The basic idea is providing a transformation of neural network features with the twofold aim of promoting the sparsity without the need of un-supervised pre-training. We validate our approach on a recent convolu-tional neural network feature, namely Regional Maximum Activations of Convolutions (R-MAC), which is a state-of-art descriptor for image retrieval. An extensive experimental evaluation conducted on standard benchmarks shows the effectiveness and efficiency of the proposed ap-proach and how it compares to state-of-the-art main-memory indexes.Source: CEUR WORKSHOP PROCEEDINGS, pp. 258-265. Virtual (online) due COVID-19, 21-24/06/2020
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2020
Other
Open Access
AIMH research activities 2020
Aloia N., Amato G., Bartalesi Lenzi 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 Fernandez 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.DOI: 10.32079/isti-ar-2020/001
Metrics:
Aloia N., Amato G., Bartalesi Lenzi 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 Fernandez 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.DOI: 10.32079/isti-ar-2020/001
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2019
Conference article
Open Access
SPLX-Perm: A Novel Permutation-Based Representation for Approximate Metric Search
Vadicamo L, Connor R, Falchi F, Gennaro C, Rabitti F
Many approaches for approximate metric search rely on a permutation-based representation of the original data objects. The main advantage of transforming metric objects into permutations is that the latter can be efficiently indexed and searched using data structures such as inverted-files and prefix trees. Typically, the permutation is obtained by ordering the identifiers of a set of pivots according to their distances to the object to be represented. In this paper, we present a novel approach to transform metric objects into permutations. It uses the object-pivot distances in combination with a metric transformation, called n-Simplex projection. The resulting permutation-based representation, named SPLX-Perm, is suitable only for the large class of metric space satisfying the n-point property. We tested the proposed approach on two benchmarks for similarity search. Our preliminary results are encouraging and open new perspectives for further investigations on the use of the n-Simplex projection for supporting permutation-based indexing.DOI: 10.1007/978-3-030-32047-8_4
Metrics:
Vadicamo L, Connor R, Falchi F, Gennaro C, Rabitti F
Many approaches for approximate metric search rely on a permutation-based representation of the original data objects. The main advantage of transforming metric objects into permutations is that the latter can be efficiently indexed and searched using data structures such as inverted-files and prefix trees. Typically, the permutation is obtained by ordering the identifiers of a set of pivots according to their distances to the object to be represented. In this paper, we present a novel approach to transform metric objects into permutations. It uses the object-pivot distances in combination with a metric transformation, called n-Simplex projection. The resulting permutation-based representation, named SPLX-Perm, is suitable only for the large class of metric space satisfying the n-point property. We tested the proposed approach on two benchmarks for similarity search. Our preliminary results are encouraging and open new perspectives for further investigations on the use of the n-Simplex projection for supporting permutation-based indexing.DOI: 10.1007/978-3-030-32047-8_4
Metrics:
See at:
dspace.stir.ac.uk | CNR IRIS | link.springer.com | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
2019
Journal article
Open Access
Supermetric search
Connor R, Vadicamo L, Cardillo Fa, Rabitti F
Metric search is concerned with the efficient evaluation of queries in metric spaces. In general, a large space of objects is arranged in such a way that, when a further object is presented as a query, those objects most similar to the query can be efficiently found. Most mechanisms rely upon the triangle inequality property of the metric governing the space. The triangle inequality property is equivalent to a finite embedding property, which states that any three points of the space can be isometrically embedded in two-dimensional Euclidean space. In this paper, we examine a class of semimetric space which is finitely four-embeddable in three-dimensional Euclidean space. In mathematics this property has been extensively studied and is generally known as the four-point property. All spaces with the four-point property are metric spaces, but they also have some stronger geometric guarantees. We coin the term supermetric(1) space as, in terms of metric search, they are significantly more tractable. Supermetric spaces include all those governed by Euclidean, Cosine,(2) Jensen-Shannon and Triangular distances, and are thus commonly used within many domains. In previous work we have given a generic mathematical basis for the supermetric property and shown how it can improve indexing performance for a given exact search structure. Here we present a full investigation into its use within a variety of different hyperplane partition indexing structures, and go on to show some more of its flexibility by examining a search structure whose partition and exclusion conditions are tailored, at each node, to suit the individual reference points and data set present there. Among the results given, we show a new best performance for exact search using a well-known benchmark. (C) 2018 Elsevier Ltd. All rights reserved.Source: INFORMATION SYSTEMS, vol. 80, pp. 108-123
DOI: 10.1016/j.is.2018.01.002
DOI: 10.48550/arxiv.1707.08361
Metrics:
Connor R, Vadicamo L, Cardillo Fa, Rabitti F
Metric search is concerned with the efficient evaluation of queries in metric spaces. In general, a large space of objects is arranged in such a way that, when a further object is presented as a query, those objects most similar to the query can be efficiently found. Most mechanisms rely upon the triangle inequality property of the metric governing the space. The triangle inequality property is equivalent to a finite embedding property, which states that any three points of the space can be isometrically embedded in two-dimensional Euclidean space. In this paper, we examine a class of semimetric space which is finitely four-embeddable in three-dimensional Euclidean space. In mathematics this property has been extensively studied and is generally known as the four-point property. All spaces with the four-point property are metric spaces, but they also have some stronger geometric guarantees. We coin the term supermetric(1) space as, in terms of metric search, they are significantly more tractable. Supermetric spaces include all those governed by Euclidean, Cosine,(2) Jensen-Shannon and Triangular distances, and are thus commonly used within many domains. In previous work we have given a generic mathematical basis for the supermetric property and shown how it can improve indexing performance for a given exact search structure. Here we present a full investigation into its use within a variety of different hyperplane partition indexing structures, and go on to show some more of its flexibility by examining a search structure whose partition and exclusion conditions are tailored, at each node, to suit the individual reference points and data set present there. Among the results given, we show a new best performance for exact search using a well-known benchmark. (C) 2018 Elsevier Ltd. All rights reserved.Source: INFORMATION SYSTEMS, vol. 80, pp. 108-123
DOI: 10.1016/j.is.2018.01.002
DOI: 10.48550/arxiv.1707.08361
Metrics:
See at:
arXiv.org e-Print Archive | Information Systems | CNR IRIS | ISTI Repository | www.sciencedirect.com | Information Systems | doi.org | CNR IRIS | CNR IRIS
2019
Conference article
Open Access
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.
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.
See at:
CNR IRIS | ISTI Repository | www.ital-ia.it | CNR IRIS
2019
Other
Open Access
AIMIR 2019 Research Activities
Amato G, Bolettieri P, Carrara F, Ciampi L, Di Benedetto M, Debole F, Falchi F, Gennaro C, Lagani G, Massoli Fv, 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.
Amato G, Bolettieri P, Carrara F, Ciampi L, Di Benedetto M, Debole F, Falchi F, Gennaro C, Lagani G, Massoli Fv, 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.
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2018
Contribution to book
Open Access
How data mining and machine learning evolved from relational data base to data science
Amato G, Candela L, Castelli D, Esuli A, Falchi F, Gennaro C, Giannotti F, Monreale A, Nanni M, Pagano P, Pappalardo L, Pedreschi D, Pratesi F, Rabitti F, Rinzivillo S, Rossetti G, Ruggieri S, Sebastiani F, Tesconi M
During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.Source: STUDIES IN BIG DATA, pp. 287-306
DOI: 10.1007/978-3-319-61893-7_17
Metrics:
Amato G, Candela L, Castelli D, Esuli A, Falchi F, Gennaro C, Giannotti F, Monreale A, Nanni M, Pagano P, Pappalardo L, Pedreschi D, Pratesi F, Rabitti F, Rinzivillo S, Rossetti G, Ruggieri S, Sebastiani F, Tesconi M
During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.Source: STUDIES IN BIG DATA, pp. 287-306
DOI: 10.1007/978-3-319-61893-7_17
Metrics:
See at:
arpi.unipi.it | CNR IRIS | link.springer.com | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
2018
Conference article
Open Access
Counting vehicles with cameras
Ciampi L, Amato G, Falchi F, Gennaro C, Rabitti F
This paper aims to develop a method that can accurately count vehicles from images of parking areas captured by smart cameras. To this end, we have proposed a deep learning-based approach for car detection that permits the input images to be of arbitrary perspectives, illumination, and occlusions. No other information about the scenes is needed, such as the position of the parking lots or the perspective maps. This solution is tested using Counting CNRPark-EXT, a new dataset created for this specific task and that is another contribution to our research. Our experiments show that our solution outperforms the state-of-the-art approaches.Source: CEUR WORKSHOP PROCEEDINGS, pp. 1-8. Castellaneta Marina - Taranto - Italy, 24-27/06/2018
Ciampi L, Amato G, Falchi F, Gennaro C, Rabitti F
This paper aims to develop a method that can accurately count vehicles from images of parking areas captured by smart cameras. To this end, we have proposed a deep learning-based approach for car detection that permits the input images to be of arbitrary perspectives, illumination, and occlusions. No other information about the scenes is needed, such as the position of the parking lots or the perspective maps. This solution is tested using Counting CNRPark-EXT, a new dataset created for this specific task and that is another contribution to our research. Our experiments show that our solution outperforms the state-of-the-art approaches.Source: CEUR WORKSHOP PROCEEDINGS, pp. 1-8. Castellaneta Marina - Taranto - Italy, 24-27/06/2018
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2017
Journal article
Open Access
Hilbert exclusion: improved metric search through finite isometric embeddings
Connor R, Cardillo F A, Vadicamo L, Rabitti F
Most research into similarity search in metric spaces relies on the triangle inequality property. This property allows the space to be arranged according to relative distances to avoid searching some subspaces. We show that many common metric spaces, notably including those using Euclidean and Jensen-Shannon distances, also have a stronger property, sometimes called the four-point property: In essence, these spaces allow an isometric embedding of any four points in three-dimensional Euclidean space, as well as any three points in two-dimensional Euclidean space. In fact, we show that any space that is isometrically embeddable in Hilbert space has the stronger property. This property gives stronger geometric guarantees, and one in particular, which we name the Hilbert Exclusion property, allows any indexing mechanism which uses hyperplane partitioning to perform better. One outcome of this observation is that a number of state-of-the-art indexing mechanisms over high-dimensional spaces can be easily refined to give a significant increase in performance; furthermore, the improvement given is greater in higher dimensions. This therefore leads to a significant improvement in the cost of metric search in these spaces.Source: ACM TRANSACTIONS ON INFORMATION SYSTEMS, vol. 35 (issue 3), pp. 17-27
DOI: 10.1145/3001583
DOI: 10.48550/arxiv.1604.08640
Metrics:
Connor R, Cardillo F A, Vadicamo L, Rabitti F
Most research into similarity search in metric spaces relies on the triangle inequality property. This property allows the space to be arranged according to relative distances to avoid searching some subspaces. We show that many common metric spaces, notably including those using Euclidean and Jensen-Shannon distances, also have a stronger property, sometimes called the four-point property: In essence, these spaces allow an isometric embedding of any four points in three-dimensional Euclidean space, as well as any three points in two-dimensional Euclidean space. In fact, we show that any space that is isometrically embeddable in Hilbert space has the stronger property. This property gives stronger geometric guarantees, and one in particular, which we name the Hilbert Exclusion property, allows any indexing mechanism which uses hyperplane partitioning to perform better. One outcome of this observation is that a number of state-of-the-art indexing mechanisms over high-dimensional spaces can be easily refined to give a significant increase in performance; furthermore, the improvement given is greater in higher dimensions. This therefore leads to a significant improvement in the cost of metric search in these spaces.Source: ACM TRANSACTIONS ON INFORMATION SYSTEMS, vol. 35 (issue 3), pp. 17-27
DOI: 10.1145/3001583
DOI: 10.48550/arxiv.1604.08640
Metrics:
See at:
arXiv.org e-Print Archive | doi.acm.org | CNR IRIS | ISTI Repository | ACM Transactions on Information Systems | ACM Transactions on Information Systems | doi.org | CNR IRIS | CNR IRIS
2017
Conference article
Restricted
Searching and annotating 100M images with YFCC100M-HNfc6 and MI-File
Amato G, Falchi F, Gennaro C, Rabitti F
We present an image search engine that allows searching by similarity about 100M images included in the YFCC100M dataset, and annotate query images. Image similarity search is performed using YFCC100M-HNfc6, the set of deep features we extracted from the YFCC100M dataset, which was indexed using the MI-File index for efficient similarity searching. A metadata cleaning algorithm, that uses visual and textual analysis, was used to select from the YFCC100M dataset a relevant subset of images and associated annotations, to create a training set to perform automatic textual annotation of submitted queries. The on-line image and annotation system demonstrates the effectiveness of the deep features for assessing conceptual similarity among images, the effectiveness of the metadata cleaning algorithm, to identify a relevant training set for annotation, and the efficiency and accuracy of the MI-File similarity index techniques, to search and annotate using a dataset of 100M images, with very limited computing resources.DOI: 10.1145/3095713.3095740
Metrics:
Amato G, Falchi F, Gennaro C, Rabitti F
We present an image search engine that allows searching by similarity about 100M images included in the YFCC100M dataset, and annotate query images. Image similarity search is performed using YFCC100M-HNfc6, the set of deep features we extracted from the YFCC100M dataset, which was indexed using the MI-File index for efficient similarity searching. A metadata cleaning algorithm, that uses visual and textual analysis, was used to select from the YFCC100M dataset a relevant subset of images and associated annotations, to create a training set to perform automatic textual annotation of submitted queries. The on-line image and annotation system demonstrates the effectiveness of the deep features for assessing conceptual similarity among images, the effectiveness of the metadata cleaning algorithm, to identify a relevant training set for annotation, and the efficiency and accuracy of the MI-File similarity index techniques, to search and annotate using a dataset of 100M images, with very limited computing resources.DOI: 10.1145/3095713.3095740
Metrics:
See at:
dl.acm.org | doi.org | CNR IRIS | CNR IRIS
2017
Conference article
Open Access
High-dimensional simplexes for supermetric search
Connor R, Vadicamo L, Rabitti F
In a metric space, triangle inequality implies that, for any three objects, a triangle with edge lengths corresponding to their pairwise distances can be formed. The n-point property is a generalisation of this where, for any (n+1) objects in the space, there exists an n-dimensional simplex whose edge lengths correspond to the distances among the objects. In general, metric spaces do not have this property; however in 1953, Blumenthal showed that any semi-metric space which is isometrically embeddable in a Hilbert space also has the n-point property. We have previously called such spaces supermetric spaces, and have shown that many metric spaces are also supermetric, including Euclidean, Cosine, Jensen-Shannon and Triangular spaces of any dimension. Here we show how such simplexes can be constructed from only their edge lengths, and we show how the geometry of the simplexes can be used to determine lower and upper bounds on unknown distances within the original space. By increasing the number of dimensions, these bounds converge to the true distance. Finally we show that for any Hilbert-embeddable space, it is possible to construct Euclidean spaces of arbitrary dimensions, from which these lower and upper bounds of the original space can be determined. These spaces may be much cheaper to query than the original. For similarity search, the engineering tradeoffs are good: we show significant reductions in data size and metric cost with little loss of accuracy, leading to a significant overall improvement in exact search performance.DOI: 10.1007/978-3-319-68474-1_7
DOI: 10.48550/arxiv.1707.08370
Metrics:
Connor R, Vadicamo L, Rabitti F
In a metric space, triangle inequality implies that, for any three objects, a triangle with edge lengths corresponding to their pairwise distances can be formed. The n-point property is a generalisation of this where, for any (n+1) objects in the space, there exists an n-dimensional simplex whose edge lengths correspond to the distances among the objects. In general, metric spaces do not have this property; however in 1953, Blumenthal showed that any semi-metric space which is isometrically embeddable in a Hilbert space also has the n-point property. We have previously called such spaces supermetric spaces, and have shown that many metric spaces are also supermetric, including Euclidean, Cosine, Jensen-Shannon and Triangular spaces of any dimension. Here we show how such simplexes can be constructed from only their edge lengths, and we show how the geometry of the simplexes can be used to determine lower and upper bounds on unknown distances within the original space. By increasing the number of dimensions, these bounds converge to the true distance. Finally we show that for any Hilbert-embeddable space, it is possible to construct Euclidean spaces of arbitrary dimensions, from which these lower and upper bounds of the original space can be determined. These spaces may be much cheaper to query than the original. For similarity search, the engineering tradeoffs are good: we show significant reductions in data size and metric cost with little loss of accuracy, leading to a significant overall improvement in exact search performance.DOI: 10.1007/978-3-319-68474-1_7
DOI: 10.48550/arxiv.1707.08370
Metrics:
See at:
arXiv.org e-Print Archive | dspace.stir.ac.uk | CNR IRIS | link.springer.com | ISTI Repository | doi.org | doi.org | CNR IRIS | CNR IRIS
2016
Conference article
Open Access
Supermetric search with the four-point property
Connor R, Vadicamo L, Cardillo Fa, Rabitti F
Metric indexing research is concerned with the efficient evaluation of queries in metric spaces. In general, a large space of objects is arranged in such a way that, when a further object is presented as a query, those objects most similar to the query can be efficiently found. Most such mechanisms rely upon the triangle inequality property of the metric governing the space. The triangle inequality property is equivalent to a finite embedding property, which states that any three points of the space can be isometrically embedded in two-dimensional Euclidean space. In this paper, we examine a class of semimetric space which is finitely 4-embeddable in three-dimensional Euclidean space. In mathematics this property has been extensively studied and is generally known as the four-point property. All spaces with the four-point property are metric spaces, but they also have some stronger geometric guarantees. We coin the term supermetric space as, in terms of metric search, they are significantly more tractable. We show some stronger geometric guarantees deriving from the four-point property which can be used in indexing to great effect, and show results for two of the SISAP benchmark searches that are substantially better than any previously published.DOI: 10.1007/978-3-319-46759-7_4
Metrics:
Connor R, Vadicamo L, Cardillo Fa, Rabitti F
Metric indexing research is concerned with the efficient evaluation of queries in metric spaces. In general, a large space of objects is arranged in such a way that, when a further object is presented as a query, those objects most similar to the query can be efficiently found. Most such mechanisms rely upon the triangle inequality property of the metric governing the space. The triangle inequality property is equivalent to a finite embedding property, which states that any three points of the space can be isometrically embedded in two-dimensional Euclidean space. In this paper, we examine a class of semimetric space which is finitely 4-embeddable in three-dimensional Euclidean space. In mathematics this property has been extensively studied and is generally known as the four-point property. All spaces with the four-point property are metric spaces, but they also have some stronger geometric guarantees. We coin the term supermetric space as, in terms of metric search, they are significantly more tractable. We show some stronger geometric guarantees deriving from the four-point property which can be used in indexing to great effect, and show results for two of the SISAP benchmark searches that are substantially better than any previously published.DOI: 10.1007/978-3-319-46759-7_4
Metrics:
See at:
CORE (RIOXX-UK Aggregator) | CNR IRIS | link.springer.com | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
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: CEUR WORKSHOP PROCEEDINGS. 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: CEUR WORKSHOP PROCEEDINGS. Venezia, Italy, 30-31 May 2016
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2016
Conference article
Open Access
Indexing 100M images with deep features and MI-File
Amato G, Falchi F, Gennaro C, Rabitti F
In the context of the Multimedia Commons initiative, we extracted and indexed deep features of about 100M images uploaded on Flickr between 2004 and 2014 and published under a Creative Commons commercial or noncommercial license. The extracted features and an online demo built using the MI-File approximated data structure are both publicly available. The online CBIR system demonstrates the effectiveness of the deep features and the efficiency of the indexing approach.Source: CEUR WORKSHOP PROCEEDINGS. Venezia, Italy, 30-31 May 2016
Amato G, Falchi F, Gennaro C, Rabitti F
In the context of the Multimedia Commons initiative, we extracted and indexed deep features of about 100M images uploaded on Flickr between 2004 and 2014 and published under a Creative Commons commercial or noncommercial license. The extracted features and an online demo built using the MI-File approximated data structure are both publicly available. The online CBIR system demonstrates the effectiveness of the deep features and the efficiency of the indexing approach.Source: CEUR WORKSHOP PROCEEDINGS. Venezia, Italy, 30-31 May 2016
See at:
ceur-ws.org | CNR IRIS | CNR IRIS