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2019 Other 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 People


2014 Conference object Unknown

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

See at: CNR People | www.eagle-network.eu


2014 Conference object Unknown

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

See at: www.ceeol.com | CNR People


2015 Conference object Unknown

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

See at: CNR People | www.scopus.com


2016 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

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2016 Conference object Unknown

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

See at: CNR People | www.scopus.com


2017 Report Unknown

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.

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2018 Report Open Access OPEN

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: SMART NEWS - Deliverable D3.3, 2018

See at: ISTI Repository Open Access | CNR People


2018 Conference object Open Access OPEN

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

See at: ISTI Repository Open Access | DOI Resolver | link.springer.com | CNR People


2014 Conference object Unknown

Some theoretical and experimental observations on permutation spaces and similarity search
Amato G., Falchi F., Rabitti F., Vadicamo L.
Permutation based approaches represent data objects as ordered lists of predefined reference objects. Similarity queries are executed by searching for data objects whose permutation representation is similar to the query one. Various permutation-based indexes have been recently proposed. They typically allow high efficiency with acceptable effectiveness. Moreover, various parameters can be set in order to find an optimal trade-off between quality of results and costs. In this paper we studied the permutation space without referring to any particular index structure focusing on both theoretical and experimental aspects. We used both synthetic and real-word datasets for our experiments. The results of this work are relevant in both developing and setting parameters of permutation-based similarity searching approaches.Source: SISAP 2014 - Similarity Search and Applications. 7th International Conference, pp. 37–49, Los Cabos, Mexico, 29-31 Ottobre 2014
DOI: 10.1007/978-3-319-11988-5_4

See at: DOI Resolver | CNR People | www.scopus.com


2015 Conference object Unknown

Searching the EAGLE epigraphic material through image recognition via a mobile device
Bolettieri P., Casarosa V., Falchi F., Vadicamo L., Martineau P., Orlandi S., Santucci R.
This demonstration paper describes the mobile application developed by the EAGLE project to increase the use and visibility of its epigraphic material. The EAGLE project (European network of Ancient Greek and Latin Epigraphy) is gathering a comprehensive collection of inscriptions (about 80 % of the surviving material) and making it accessible through a user-friendly portal, which supports searching and browsing of the epigraphic material. In order to increase the usefulness and visibility of its content, EAGLE has developed also a mobile application to enable tourists and scholars to obtain detailed information about the inscriptions they are looking at by taking pictures with their smartphones and sending them to the EAGLE portal for recognition. In this demonstration paper we describe the EAGLE mobile application and give an outline of its features and its architecture.Source: Similarity Search and Applications. 8th International Conference, pp. 351–354, Glasgow, UK, 12-14/10/2015
DOI: 10.1007/978-3-319-25087-8_35
Project(s): EAGLE

See at: DOI Resolver | link.springer.com | CNR People


2016 Conference object Unknown

How effective are aggregation methods on binary features?
Amato G., Falchi F., Vadicamo L
During the last decade, various local features have been proposed and used to support Content Based Image Retrieval and object recognition tasks. Local features allow to effectively match local structures between images, but the cost of extraction and pairwise comparison of the local descriptors becomes a bottleneck when mobile devices and/or large database are used. Two major directions have been followed to improve efficiency of local features based approaches. On one hand, the cost of extracting, representing and matching local visual descriptors has been reduced by defining binary local features. On the other hand, methods for quantizing or aggregating local features have been proposed to scale up image matching on very large scale. In this paper, we performed an extensive comparison of the state-of-the-art aggregation methods applied to ORB binary descriptors. Our results show that the use of aggregation methods on binary local features is generally effective even if, as expected, there is a loss of performance compared to the same approaches applied to non-binary features. However, aggregations of binary feature represent a worthwhile option when one need to use devices with very low CPU and memory resources, as mobile and wearable devices.Source: International Conference on Computer Vision Theory and Applications, pp. 556–573, Roma, Italy, 27-29 February 2016
DOI: 10.5220/0005719905660573
Project(s): EAGLE

See at: DOI Resolver | CNR People | www.scitepress.org


2017 Article Open Access OPEN

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 35 (2017): 17–27. doi:10.1145/3001583
DOI: 10.1145/3001583

See at: arXiv.org e-Print Archive Open Access | doi.acm.org | DOI Resolver | CNR People


2016 Conference object Unknown

Deep permutations: Deep convolutional neural networks and permutation-based indexing
Amato G, Falchi F., Gennaro C., Vadicamo L.
The activation of the Deep Convolutional Neural Networks hidden layers can be successfully used as features, often referred as Deep Features, in generic visual similarity search tasks. Recently scientists have shown that permutation-based methods offer very good performance in indexing and supporting approximate similarity search on large database of objects. Permutation-based approaches represent metric objects as sequences (permutations) of reference objects, chosen from a predefined set of data. However, associating objects with permutations might have a high cost due to the distance calculation between the data objects and the reference objects. In this work, we propose a new approach to generate permutations at a very low computational cost, when objects to be indexed are Deep Features. We show that the permutations generated using the proposed method are more effective than those obtained using pivot selection criteria specifically developed for permutation-based methods.Source: Similarity Search and Applications. 9th International Conference, pp. 93–106, Tokyo, Japan, 24-26 October 2016
DOI: 10.1007/978-3-319-46759-7_7

See at: DOI Resolver | CNR People | www.scopus.com


2016 Conference object Unknown

Supermetric search with the four-point property
Connor R., Vadicamo L., Cardillo F. A., 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.Source: Similarity Search and Applications. 9th International Conference, pp. 51–64, Tokyo, Japan, 24-26 October 2016
DOI: 10.1007/978-3-319-46759-7_4

See at: DOI Resolver | CNR People | www.scopus.com


2016 Conference object Unknown

Using Apache Lucene to search vector of locally aggregated descriptors
Amato G., Bolettieri P., Falchi F., Gennaro C., Vadicamo L.
Surrogate Text Representation (STR) is a profitable solution to efficient similarity search on metric space using conventional text search engines, such as Apache Lucene. This technique is based on comparing the permutations of some reference objects in place of the original metric distance. However, the Achilles heel of STR approach is the need to reorder the result set of the search according to the metric distance. This forces to use a support database to store the original objects, which requires efficient random I/O on a fast secondary memory (such as flash-based storages). In this paper, we propose to extend the Surrogate Text Representation to specifically address a class of visual metric objects known as Vector of Locally Aggregated Descriptors (VLAD). This approach is based on representing the individual sub-vectors forming the VLAD vector with the STR, providing a finer representation of the vector and enabling us to get rid of the reordering phase. The experiments on a publ icly available dataset show that the extended STR outperforms the baseline STR achieving satisfactory performance near to the one obtained with the original VLAD vectorsSource: 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 383–392, Roma, Italy, 27-29 February 2016
DOI: 10.5220/0005722503830392
Project(s): EAGLE

See at: DOI Resolver | CNR People | www.scitepress.org


2018 Conference object Open Access OPEN

Re-ranking Permutation-Based Candidate Sets with the n-Simplex Projection
Amato G., Chavez E., Connor R., Falchi F., Gennaro C., Vadicamo L.
In the realm of metric search, the permutation-based approaches have shown very good performance in indexing and supporting approximate search on large databases. These methods embed the metric objects into a permutation space where candidate results to a given query can be efficiently identified. Typically, to achieve high effectiveness, the permutation-based result set is refined by directly comparing each candidate object to the query one. Therefore, one drawback of these approaches is that the original dataset needs to be stored and then accessed during the refining step. We propose a refining approach based on a metric embedding, called n-Simplex projection, that can be used on metric spaces meeting the n-point property. The n-Simplex projection provides upper- and lower-bounds of the actual distance, derived using the distances between the data objects and a finite set of pivots. We propose to reuse the distances computed for building the data permutations to derive these bounds and we show how to use them to improve the permutation-based results. Our approach is particularly advantageous for all the cases in which the traditional refining step is too costly, e.g. very large dataset or very expensive metric function.Source: Similarity Search and Applications. SISAP 2018, pp. 3–17, Lima, Perù, 7-9 Ottobre 2018
DOI: 10.1007/978-3-030-02224-2_1

See at: ISTI Repository Open Access | DOI Resolver | CNR People | www.scopus.com


2019 Conference object 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 | DOI Resolver | link.springer.com | CNR People


2016 Article Unknown

Visual Recognition of Ancient Inscriptions Using Convolutional Neural Network and Fisher Vector
Amato G., Falchi F., Vadicamo L.
By bringing together the most prominent European institutions and archives in the field of Classical Latin and Greek epigraphy, the EAGLE project has collected the vast majority of the surviving Greco-Latin inscriptions into a single readily-searchable database. Text-based search engines are typically used to retrieve information about ancient inscriptions (or about other artifacts). These systems require that the users formulate a text query that contains information such as the place where the object was found or where it is currently located. Conversely, visual search systems can be used to provide information to users (like tourists and scholars) in a most intuitive and immediate way, just using an image as query. In this article, we provide a comparison of several approaches for visual recognizing ancient inscriptions. Our experiments, conducted on 17, 155 photos related to 14, 560 inscriptions, show that BoW and VLAD are outperformed by both Fisher Vector (FV) and Convolutional Neural Network (CNN) features. More interestingly, combining FV and CNN features into a single image representation allows achieving very high effectiveness by correctly recognizing the query inscription in more than 90% of the cases. Our results suggest that combinations of FV and CNN can be also exploited to effectively perform visual retrieval of other types of objects related to cultural heritage such as landmarks and monuments.Source: ACM journal on computing and cultural heritage (Print) 9 (2016): 21–24. doi:10.1145/2964911
DOI: 10.1145/2964911
Project(s): EAGLE

See at: dl.acm.org | DOI Resolver | CNR People


2017 Conference object Unknown

Cross-media learning for image sentiment analysis in the wild
Vadicamo L., Carrara F., Falchi F., Cimino A., Dell'Orletta F., Cresci S., Tesconi M.
Much progress has been made in the field of sentiment analysis in the past years. Researchers relied on textual data for this task, while only recently they have started investigating approaches to predict sentiments from multimedia content. With the increasing amount of data shared on social media, there is also a rapidly growing interest in approaches that work "in the wild", i.e. that are able to deal with uncontrolled conditions. In this work, we faced the challenge of training a visual sentiment classifier starting from a large set of user-generated and unlabeled contents. In particular, we collected more than 3 million tweets containing both text and images, and we leveraged on the sentiment polarity of the textual contents to train a visual sentiment classifier. To the best of our knowledge, this is the first time that a cross-media learning approach is proposed and tested in this context. We assessed the validity of our model by conducting comparative studies and evaluations on a benchmark for visual sentiment analysis. Our empirical study shows that although the text associated to each image is often noisy and weakly correlated with the image content, it can be profitably exploited to train a deep Convolutional Neural Network that effectively predicts the sentiment polarity of previously unseen images.Source: ICCV 2017 IEEE International Conference on Computer Vision Workshops, Venezia, Italy, 22-29 October 2017

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