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2021 Journal article Open Access OPEN

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

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


2019 Conference article Open Access OPEN

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

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


2019 Software Unknown

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

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


2019 Conference article Open Access OPEN

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

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


2019 Journal article Open Access OPEN

Virtual restoration and content analysis of ancient degraded manuscripts
Tonazzini A., Savino P., Salerno E., Hanif M., Debole F.
In recent years, extensive campaigns of digitization of the documental heritage conserved in libraries and archives have been performed, with the primary goal to ensure the preservation and fruition of this important part of the human cultural and historical patrimony. Besides protecting conservation, the availability of high quality digital copies has increasingly stimulated the use of image processing techniques, to perform a number of operations on documents and manuscripts, without harming the often precious and fragile originals. Among those, virtual restoration tasks are crucial, as they facilitate the traditional work of philologists and paleographers, and constitute a first step towards an automatic analysis of the written contents. Here we report our experience in this field, referring, as a case study, to the problem of removing one of the most frequent and impairing degradations affecting ancient manuscripts, i.e., the bleed-through distortion.We show that techniques of blind source separation are versatile tools to either cancel these unwanted interferences or isolate specific features for content analysis goals. Specialized algorithms, based on recto-verso models and sparse image representation, are then shown to be able to perform a fine and selective removal of the degradation, while preserving the original appearance of the manuscript.Source: International Journal of Information Science and Technology 3 (2019): 16–25.

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.innove.org Open Access


2019 Conference article Open Access OPEN

Intelligenza Artificiale per Ricerca in Big Multimedia Data
Carrara F., Amato G., Debole F., Di Benedetto M., Falchi F., Gennaro C., Messina N.
La diffusa produzione di immagini e media digitali ha reso necessario l'utilizzo di metodi automatici di analisi e indicizzazione su larga scala per la loro fruzione. Il gruppo AIMIR dell'ISTI-CNR si è specializzato da anni in questo ambito ed ha abbracciato tecniche di Deep Learning basate su reti neurali artificiali per molteplici aspetti di questa disciplina, come l'analisi, l'annotazione e la descrizione automatica di contenuti visuali e il loro recupero su larga scala.Source: Ital-IA, Roma, 18/3/2019, 19/3/2019

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


2019 Report Open Access OPEN

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

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


2019 Journal article Open Access OPEN

A data model and a cataloguing, storage and retrieval system for ancient document archives
Savino P., Tonazzini A., Debole F.
Digitalization of ancient manuscripts is becoming a common practice in many archives and libraries, mainly for preservation purposes. This opens many new opportunities for the diffusion of these precious cultural assets, since several scholars and researchers, as well as the general public, may access and use them for research purposes, for study, and for general information. This is made possible if the documents, their descriptions, and the result of all processing activities performed on them are acquired at a good quality and can be easily accessed by using simple and powerful retrieval mechanisms. Acquired manuscripts suffer of degradations that may require different types of elaborations on the digital images, to improve their visual quality and legibility, or to discover hidden text that is not visible. Natural Language Processing requires the creation of transcriptions of the text contained in the manuscript, as well as encoding of the document structure and creation of user annotations. This paper presents a document management system and a metadata schema that make possible the storage and content-based retrieval of original documents, elaborations performed to improve their readability, textual transcriptions, and linguistic annotations. The archive will offer the possibility of describing, storing and accessing all the available manuscript versions, document transcriptions and annotations, and to search and retrieve documents based on all this information.Source: International Journal of Information Science and Technology 3 (2019): 6–15.

See at: innove.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2019 Conference article Open Access OPEN

Image analysis in technical documentation
Carrara F., Debole F., Gennaro C., Amato G.
In the era of Big Data, manufacturing companies are overwhelmed by a lot of disorganized information: the large amount of digital content that is increasingly available in the manufacturing process makes the retrieval of accurate information a critical issue. In this context, and thanks also to the Industry 4.0 campaign, the Italian manufacturing industries have made a lot of effort to ameliorate their knowledge management system using the most recent technologies, like big data analysis and machine learning methods. This paper presents the on-going work done within the ADA project, with special emphasis on the specific image analysis work carried out to extract information from images contained in the so different document of the manufacturing companies, partners of the project.Source: SEBD 2019 - Italian Symposium on Advanced Database Systems, Castiglione della Pescaia (Grosseto), Italy, 16-19 July, 2019

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


2018 Conference article Open Access OPEN

Archiving and retrieving digital elaborations of ancient manuscripts
Savino P., Tonazzini A., Debole F., Salerno E.
Digitalization of ancient manuscripts is becoming a standard in libraries and archives. In many cases, manuscripts suffer of degradations that may require performing different types of elaborations on the digital images, in order to improve their legibility and analyze their contents. Digital archives containing digital images of manuscripts and all the elaborations performed on these images are thus of primary importance for a complete exploitation of all available information regarding the manuscripts themselves. This paper presents a metadata schema suitable for the management of such an archive. The archive will offer the possibility of describing, storing and accessing all the available manuscript versions, and to search them based on their content.Source: CiST 2018 - IEEE 5th International Congress on Information Science and Technology, pp. 172–177, Marrakech, Marocco, 21-27 October 2018
DOI: 10.1109/cist.2018.8596505

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


2018 Conference article Open Access OPEN

A first step towards NLP from digitized manuscripts: virtual restoration
Debole F., Hanif M., Salerno E., Savino P., Tonazzini A.
Digitization of the documental heritage conserved in libraries and archives is a common practice, in order to ensure the preservation and fruition of this extended part of the human cultural and historical patrimony. For the most precious, fragile and difficult to read and decipher manuscripts, specialized though portable digitization equipment, such as high resolution multispectral/hyperspectral cameras, is nowadays available. Digitization made it possible the increasingly extensive use of digital image processing techniques, to perform a number of virtual restoration tasks, which constitute a first, often necessary step prior subsequent automatic analysis of the writing contents, with the ultimate goal to perform automatic transcription and/or natural language processing tasks. Here we report our experience in this field, referring, as a case study, to the problem of removing one of the most frequent and impairing degradation affecting many ancient manuscripts, i.e., the bleed-through distortion. In this case, virtual restoration gives also the immediate benefit to facilitate the work of philologists and paleographers interested in examining and transcribing the manuscript in a traditional way.Source: CiST 2018 - IEEE 5th International Congress on Information Science and Technology, pp. 188–193, Marrakech, Marocco, 21-27 October 2018
DOI: 10.1109/cist.2018.8596494

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


2017 Report Open Access OPEN

PARTHENOS - Design of the joint resource registry. Deliverable D5.2 PARTHENOS
Aloia N., Candela L., Debole F., Frosini L., Lorenzini M., Pagano P.
The activity of design of the Joint Resource Registry is propaedeutic to the building phase of a comprehensive inventory of resources and is the result of two different activities: o A survey of resources (datasets, collections, infrastructures, services) available in the archaeological context; o The definition of the main entities for the PARTHENOS data model taking into account the ontology defined in T5.1. In this document, we present a description of the existing registries in the humanities area, derived from the analysis conducted in the first activity. The goal of the activity was to identify the main descriptive conceptual entities and features of existing resources that would be useful for the definition of the PARTHENOS Registry Data Model. This document is organized as follows: Section 2 describes the best-known standards used in the definition of registry models; Section 3 describes the survey carried out of existing registries in the thematic area of the PARTHENOS project; Section 4 contains a detailed description of the surveyed registries; Section 5 summarizes the main entities and functionalities of the analysed registries; Section 6 presents the PARTHENOS Joint Resource Registry Data Model; and finally, Section 7 presents the architecture of the Joint Resource Registry service that implements the model.Source: Project report, PARTHENOS, Deliverable D5.2, pp.1, 2017
Project(s): PARTHENOS via OpenAIRE

See at: CNR ExploRA Open Access | www.parthenos-project.eu Open Access


2017 Journal article Open Access OPEN

Mapping the ARIADNE catalogue data model to CIDOC CRM: Bridging resource discovery and item-level access
Aloia N., Debole F., Felicetti A., Galluccio I., Theodoridou M.
ARIADNE is a European project aiming to integrate existing archaeological research infrastructures, services and distributed datasets, and to develop new technologies and tools to improve archaeological research methodology. The ARIADNE registry contains information about resources available among the various partners of the project and the metadata repository, which contains item level information of these resources. In order to provide an advanced discovery mechanism combining both item level and registry level information we propose a mapping from the ARIADNE Catalog Data Model, the model of the ARIADNE registry, to the CIDOC CRM, the underlying model of the metadata repository. The paper will present the requirements that led to the choice of different models for the registry and the metadata repository, will elaborate on the mapping, and will propose an integrated interface for information discovery and presentation.Source: SCIRES-IT (Roma) 7 (2017): 1–8. doi:10.2423/i22394303v7n1p1
DOI: 10.2423/i22394303v7n1p1

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.sciresit.it Open Access


2016 Conference article Open Access OPEN

Building a digital library containing digital elaborations of ancient documents
Debole F., Savino P., Tonazzini A.
Digital archives containing digitized images and detailed descriptions of cultural heritage objects are of primary importance in order to guarantee the preservation and to foster the fruition of many fragile artifacts of our culture and history. Digital processing of these images is frequently needed in order to improve their readability, to correct degradations and damages, and to analyze their contents. This paper presents a metadata schema and a metadata editor supporting the description and the archiving of all elaboration activities performed. The archive allows one to perform content based searches of the original object's descriptions as well as of the results of the elaboration activities.Source: Tenth International Conference on Digital Information Management, pp. 124–131, Jeju Island, South Korea, 21-23/10/2015
DOI: 10.1109/icdim.2015.7381855

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


2016 Conference article Restricted

Large Scale Indexing and Searching Deep Convolutional Neural Network Features
Amato G, Debole F, Falchi F, Gennaro C, Rabitti F.
Content-based image retrieval using Deep Learning has become very popular during the last few years. In this work, we propose an approach to index Deep Convolutional Neural Network Features to support efficient retrieval on very large image databases. The idea is to provide a text encoding for these features enabling the use of a text retrieval engine to perform image similarity search. In this way, we built LuQ a robust retrieval system that combines full-text search with content-based image retrieval capabilities. In order to optimize the index occupation and the query response time, we evaluated various tuning parameters to generate the text encoding. To this end, we have developed a web-based prototype to efficiently search through a dataset of 100 million of images.Source: 18th International Conference on Data Warehousing and Knowledge Discovery (DAWAK), pp. 213–224, Porto, Portugal, 06-08 September 2016
DOI: 10.1007/978-3-319-43946-4_14

See at: academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | doi.org Restricted | link.springer.com Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted


2015 Conference article Open Access OPEN

Un catalogo per la descrizione di risorse archeologiche
Aloia N., Debole F., Meghini C.
This paper discusses the registry developed by the ARIADNE project for describing the archaeological resources that are made available by the partners of the project for the purposes of discovery, access and integration on a research infrastructure. These resources include: data, services and language resources, such as metadata formats, vocabularies and mappings. The registry is addressed to cultural institutions, private or public, which wish to describe their assets in order to make them known to e-infrastructures.Source: Workshop L'integrazione dei dati archeologici digitali - Esperienze e prospettive in Italia (InDarD 2015), pp. 26–35, Lecce, Italy, 01-02/10/2015
Project(s): ARIADNE via OpenAIRE

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


2014 Conference article Restricted

Describing research data: a case study for archaeology
Aloia N., Papatheodorou C., Gavrilis D., Debole F, Meghini C.
The growth of the digital resources produced by the research activities demand the development of e-Infrastructures in which researchers can access remote facilities, select and re-use huge volumes of data and services, run complex experimental processes and share results. Data registries aim to describe uniformly the data of e-Infrastructures contributing to the re-usability and interoperability of big scientific data. However the current situation requires the development of powerful resource integration mechanisms that step beyond the principles guaranteed by the data registries standards. This paper proposes a conceptual model for describing data resources and services and extends the existing specifications for the development of data registries. The model has been implemented in the context of the ARIADNE project, a EU funded project that focuses on the integration of Archaeological digital resources all over the Europe.Source: OTM 2014 - On the Move to Meaningful Internet Systems: OTM 2014 Conferences. Confederated International Conferences: CoopIS and ODBASE 2014, pp. 768–775, Amantea, Cosenza, Italy, 27-31 October 2014
DOI: 10.1007/978-3-662-45563-0_48

See at: academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | rd.springer.com Restricted | www.scopus.com Restricted


2014 Conference article Restricted

Enriching image feature description supporting effective content-based retrieval and annotation
Debole F., Gennaro C., Savino P.
The paper describes a technique that supports efficient and effective Content-Based Image Retrieval (CBIR) in very large image archives as well as automatic image tagging. The proposed technique uses a unified representation for image visual features and for image textual descriptions. Images are clustered according to their image visual features while textual content is used to associate relevant tags to images belonging to the same cluster. The system supports image retrieval based on image query similarity, on textual queries, and on mixed mode queries composed of an image and a textual part and automatic image tagging.Source: VSMM 2014 - International Conference on Virtual Systems & Multimedia, pp. 80–87, Hong Kong, China, 8-12 December 2014
DOI: 10.1109/vsmm.2014.7136647

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


2014 Software Unknown

IMSEARCH
Debole F., Gennaro C., Savino P.
IMSEARCH is an image retrieval system based on a technique that supports efficient and effective Content-Based Image Retrieval (CBIR) in very large image archives as well as automatic image tagging. The proposed technique uses a unified representation for image visual features and for image textual descriptions. Images are clustered according to their image visual features while textual content is used to associate relevant tags to images belonging to the same cluster. The system supports image retrieval based on image query similarity, on textual queries, and on mixed mode queries composed of an image and a textual part and automatic image tagging. The prototype has been implemented in Java by using Lucene as a system for text retrieval and Mahout (https://mahout.apache.org/) for clustering.

See at: melisandre.deepfeatures.org | CNR ExploRA


2013 Contribution to book Restricted

Data interoperability and curation: the European Film Gateway Experience
Artini M., Bardi A., Biagini F., Debole F., La Bruzzo S., Manghi P., Mikulicic M., Savino P., Zoppi F.
Film archives, containing collections of cinema-related digital material, have been created in many European countries. Today, the EC Best Practice Network Project EFG (European Film Gateway) provides a single access point to 59 collections from 19 archives and across 14 European countries, for a total of 640,000 digital objects. This paper illustrates challenges and solutions in the realization of the EFG data infrastructure. These mainly concerned the curation and interoperability issues derived by the need of aggregating metadata from heterogeneous archives (different data models, hence metadata schemas, and exchange formats). EFG designed a common data model for movie information, onto which archives data models can be optimally mapped. It realizes a data infrastructure based on the D-NET software toolkit, capable of dealing with data collection, mapping, cleaning, indexing, and access provision through web portals or standard access protocols. To achieve its objectives EFG has extended D-NET with advanced tools for data curation.Source: Digital Libraries and Archives - 8th Italian Research Conference, IRCDL 2012, Bari, Italy, February 9-10, 2012, Revised Selected Papers, edited by Agosti, Maristella and Esposito, Floriana and Ferilli, Stefano and Ferro, Nicola, pp. 33–44, 2013
Project(s): EFG1914

See at: link.springer.com Restricted | CNR ExploRA Restricted