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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


2019 Conference object Open Access OPEN

Learning relationship-aware visual features
Messina N., Amato G., Carrara F., Falchi F., Gennaro C.
Relational reasoning in Computer Vision has recently shown impressive results on visual question answering tasks. On the challenging dataset called CLEVR, the recently proposed Relation Network (RN), a simple plug-and-play module and one of the state-of-the-art approaches, has obtained a very good accuracy (95.5%) answering relational questions. In this paper, we define a sub-field of Content-Based Image Retrieval (CBIR) called Relational-CBIR (R-CBIR), in which we are interested in retrieving images with given relationships among objects. To this aim, we employ the RN architecture in order to extract relation-aware features from CLEVR images. To prove the effectiveness of these features, we extended both CLEVR and Sort-of-CLEVR datasets generating a ground-truth for R-CBIR by exploiting relational data embedded into scene-graphs. Furthermore, we propose a modification of the RN module - a two-stage Relation Network (2S-RN) - that enabled us to extract relation-aware features by using a preprocessing stage able to focus on the image content, leaving the question apart. Experiments show that our RN features, especially the 2S-RN ones, outperform the RMAC state-of-the-art features on this new challenging task.Source: ECCV 2018 - European Conference on Computer Vision, pp. 486–501, Monaco, Germania, 8-14 Settembre 2018
DOI: 10.1007/978-3-030-11018-5_40

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2019 Report Open Access OPEN

SmartPark@Lucca - D5. Integrazione e sperimentazione sul campo
Amato G., Bolettieri P., Carrara F., Ciampi L., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In questo deliverable sono descritte le attività eseguite all'interno del WP3, in particolare relative al Task 3.1 - Integrazione e al Task 3.2 - Sperimentazione sul campo.Source: SmartPark@Lucca, Project Report D5., pp.1–24, 2019

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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

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2019 Conference object Open Access OPEN

Intelligenza Artificiale e Analisi Visuale per la Cyber Security
Vairo C., Amato G., Ciampi L., Falchi F., Gennaro C., Massoli F. V.
Negli ultimi anni la Cyber Security ha acquisito una connotazione sempre più vasta, andando oltre la concezione di semplice sicurezza dei sistemi informatici e includendo anche la sorveglianza e la sicurezza in senso lato, sfruttando le ultime tecnologie come ad esempio l'intelligenza artificiale. In questo contributo vengono presentate le principali attività di ricerca e alcune delle tecnologie utilizzate e sviluppate dal gruppo di ricerca AIMIR dell'ISTI-CNR, e viene fornita una panoramica dei progetti di ricerca, sia passati che attualmente attivi, in cui queste tecnologie di intelligenza artificiale vengono utilizzare per lo sviluppo di applicazioni e servizi per la Cyber Security.Source: Ital-IA, Roma, 18/3/2019, 19/3/2019

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2019 Conference object Open Access OPEN

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.Source: Ital-IA, Roma, 18/3/2019, 19/3/2019

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


2019 Conference object Open Access OPEN

Testing Deep Neural Networks on the Same-Different Task
Messina N., Amato G., Carrara F., Falchi F., Gennaro C.
Developing abstract reasoning abilities in neural networks is an important goal towards the achievement of human-like performances on many tasks. As of now, some works have tackled this problem, developing ad-hoc architectures and reaching overall good generalization performances. In this work we try to understand to what extent state-of-The-Art convolutional neural networks for image classification are able to deal with a challenging abstract problem, the so-called same-different task. This problem consists in understanding if two random shapes inside the same image are the same or not. A recent work demonstrated that simple convolutional neural networks are almost unable to solve this problem. We extend their work, showing that ResNet-inspired architectures are able to learn, while VGG cannot converge. In light of this, we suppose that residual connections have some important role in the learning process, while the depth of the network seems not so relevant. In addition, we carry out some targeted tests on the converged architectures to figure out to what extent they are able to generalize to never seen patterns. However, further investigation is needed in order to understand what are the architectural peculiarities and limits as far as abstract reasoning is concerned.Source: 2019 International Conference on Content-Based Multimedia Indexing (CBMI), Dublin, Ireland, 4/9/2019, 6/9/2019
DOI: 10.1109/CBMI.2019.8877412

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2019 Conference object Open Access OPEN

Learning Safety Equipment Detection using Virtual Worlds
Di Benedetto M., Meloni E., Amato G., Falchi F., Gennaro C.
Nowadays, the possibilities offered by state-of-The-Art deep neural networks allow the creation of systems capable of recognizing and indexing visual content with very high accuracy. Performance of these systems relies on the availability of high quality training sets, containing a large number of examples (e.g. million), in addition to the the machine learning tools themselves. For several applications, very good training sets can be obtained, for example, crawling (noisily) annotated images from the internet, or by analyzing user interaction (e.g.: on social networks). However, there are several applications for which high quality training sets are not easy to be obtained/created. Consider, as an example, a security scenario where one wants to automatically detect rarely occurring threatening events. In this respect, recently, researchers investigated the possibility of using a visual virtual environment, capable of artificially generating controllable and photo-realistic contents, to create training sets for applications with little available training images. We explored this idea to generate synthetic photo-realistic training sets to train classifiers to recognize the proper use of individual safety equipment (e.g.: worker protection helmets, high-visibility vests, ear protection devices) during risky human activities. Then, we performed domain adaptation to real images by using a very small image data set of real-world photographs. We show that training with the generated synthetic training set and using the domain adaptation step is an effective solution to address applications for which no training sets exist.Source: 2019 International Conference on Content-Based Multimedia Indexing (CBMI), Dublin, Ireland, 4/9/2019, 6/9/2019
DOI: 10.1109/CBMI.2019.8877466

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2019 Conference object Open Access OPEN

Hebbian learning meets deep convolutional neural networks
Amato G., Carrara F., Falchi F., Gennaro C., Lagani G.
Neural networks are said to be biologically inspired since they mimic the behavior of real neurons. However, several processes in state-of-the-art neural networks, including Deep Convolutional Neural Networks (DCNN), are far from the ones found in animal brains. One relevant difference is the training process. In state-of-the-art artificial neural networks, the training process is based on backpropagation and Stochastic Gradient Descent (SGD) optimization. However, studies in neuroscience strongly suggest that this kind of processes does not occur in the biological brain. Rather, learning methods based on Spike-Timing-Dependent Plasticity (STDP) or the Hebbian learning rule seem to be more plausible, according to neuroscientists. In this paper, we investigate the use of the Hebbian learning rule when training Deep Neural Networks for image classification by proposing a novel weight update rule for shared kernels in DCNNs. We perform experiments using the CIFAR-10 dataset in which we employ Hebbian learning, along with SGD, to train parts of the model or whole networks for the task of image classification, and we discuss their performance thoroughly considering both effectiveness and efficiency aspects.Source: Image Analysis and Processing - ICIAP 2019, pp. 324–334, Trento, Italia, 9/9/2019, 13/9/2019
DOI: 10.1007/978-3-030-30642-7_29

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2019 Conference object Open Access OPEN

Learning pedestrian detection from virtual worlds
Amato G., Ciampi L., Falchi F., Gennaro C., Messina N.
In this paper, we present a real-time pedestrian detection system that has been trained using a virtual environment. This is a very popular topic of research having endless practical applications and recently, there was an increasing interest in deep learning architectures for performing such a task. However, the availability of large labeled datasets is a key point for an effective train of such algorithms. For this reason, in this work, we introduced ViPeD, a new synthetically generated set of images extracted from a realistic 3D video game where the labels can be automatically generated exploiting 2D pedestrian positions extracted from the graphics engine. We exploited this new synthetic dataset fine-tuning a state-of-the-art computationally efficient Convolutional Neural Network (CNN). A preliminary experimental evaluation, compared to the performance of other existing approaches trained on real-world images, shows encouraging results.Source: Image Analysis and Processing - ICIAP 2019, pp. 302–312, Trento, Italia, 9/9/2019, 13/9/2019
DOI: 10.1007/978-3-030-30642-7_27

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2019 Conference object Open Access OPEN

Evaluation of continuous image features learned by ODE nets
Carrara F., Amato G., Falchi F., Gennaro C.
Deep-learning approaches in data-driven modeling relies on learning a finite number of transformations (and representations) of the data that are structured in a hierarchy and are often instantiated as deep neural networks (and their internal activations). State-of-the-art models for visual data usually implement deep residual learning: the network learns to predict a finite number of discrete updates that are applied to the internal network state to enrich it. Pushing the residual learning idea to the limit, ODE Net--a novel network formulation involving continuously evolving internal representations that gained the best paper award at NeurIPS 2018--has been recently proposed. Differently from traditional neural networks, in this model the dynamics of the internal states are defined by an ordinary differential equation with learnable parameters that defines a continuous transformation of the input representation. These representations can be computed using standard ODE solvers, and their dynamics can be steered to learn the input-output mapping by adjusting the ODE parameters via standard gradient-based optimization. In this work, we investigate the image representation learned in the continuous hidden states of ODE Nets. In particular, we train image classifiers including ODE-defined continuous layers and perform preliminary experiments to assess the quality, in terms of transferability and generality, of the learned image representations and compare them to standard representation extracted from residual networks. Experiments on CIFAR-10 and Tiny-ImageNet-200 datasets show that representations extracted from ODE Nets are more transferable and suggest an improved robustness to overfit.Source: Image Analysis and Processing - ICIAP 2019, pp. 432–442, Trento, Italia, 9/9/2019 - 13/9/2019
DOI: 10.1007/978-3-030-30642-7_39

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2019 Conference object Open Access OPEN

Improving Multi-scale Face Recognition Using VGGFace2
Massoli F. V., Amato G., Falchi F., Gennaro C., Vairo C.
Convolutional neural networks have reached extremely high performances on the Face Recognition task. These models are commonly trained by using high-resolution images and for this reason, their discrimination ability is usually degraded when they are tested against low-resolution images. Thus, Low-Resolution Face Recognition remains an open challenge for deep learning models. Such a scenario is of particular interest for surveillance systems in which it usually happens that a low-resolution probe has to be matched with higher resolution galleries. This task can be especially hard to accomplish since the probe can have resolutions as low as 8, 16 and 24 pixels per side while the typical input of state-of-the-art neural network is 224. In this paper, we described the training campaign we used to fine-tune a ResNet-50 architecture, with Squeeze-and-Excitation blocks, on the tasks of very low and mixed resolutions face recognition. For the training process we used the VGGFace2 dataset and then we tested the performance of the final model on the IJB-B dataset; in particular, we tested the neural network on the 1:1 verification task. In our experiments we considered two different scenarios: (1) probe and gallery with same resolution; (2) probe and gallery with mixed resolutions. Experimental results show that with our approach it is possible to improve upon state-of-the-art models performance on the low and mixed resolution face recognition tasks with a negligible loss at very high resolutions.Source: BioFor Workshop on Recent Advances in Digital Security: Biometrics and Forensics, pp. 21–29, Trento, Berlino, 8/9/2019
DOI: 10.1007/978-3-030-30754-7_3

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2019 Article Open Access OPEN

Large-scale instance-level image retrieval
Amato G., Carrara F., Falchi F., Gennaro C., Vadicamo L.
The great success of visual features learned from deep neural networks has led to a significant effort to develop efficient and scalable technologies for image retrieval. Nevertheless, its usage in large-scale Web applications of content-based retrieval is still challenged by their high dimensionality. To overcome this issue, some image retrieval systems employ the product quantization method to learn a large-scale visual dictionary from a training set of global neural network features. These approaches are implemented in main memory, preventing their usage in big-data applications. The contribution of the work is mainly devoted to investigating some approaches to transform neural network features into text forms suitable for being indexed by a standard full-text retrieval engine such as Elasticsearch. The basic idea of our approaches relies on a transformation of neural network features with the twofold aim of promoting the sparsity without the need of unsupervised pre-training. We validate our approach on a recent convolutional neural network feature, namely Regional Maximum Activations of Convolutions (R-MAC), which is a state-of-art descriptor for image retrieval. Its effectiveness has been proved through several instance-level retrieval benchmarks. An extensive experimental evaluation conducted on the standard benchmarks shows the effectiveness and efficiency of the proposed approach and how it compares to state-of-the-art main-memory indexes.Source: Information processing & management (2019). doi:10.1016/j.ipm.2019.102100
DOI: 10.1016/j.ipm.2019.102100

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2019 Conference object Open Access OPEN

Face Verification and Recognition for Digital Forensics and Information Security
Amato G., Falchi F., Gennaro C., Massoli F. V., Passalis N., Tefas A., Trivilini A., Vairo C.
In this paper, we present an extensive evaluation of face recognition and verification approaches performed by the European COST Action MULTI-modal Imaging of FOREnsic SciEnce Evidence (MULTI-FORESEE). The aim of the study is to evaluate various face recognition and verification methods, ranging from methods based on facial landmarks to state-of-the-art off-the-shelf pre-trained Convolutional Neural Networks (CNN), as well as CNN models directly trained for the task at hand. To fulfill this objective, we carefully designed and implemented a realistic data acquisition process, that corresponds to a typical face verification setup, and collected a challenging dataset to evaluate the real world performance of the aforementioned methods. Apart from verifying the effectiveness of deep learning approaches in a specific scenario, several important limitations are identified and discussed through the paper, providing valuable insight for future research directions in the field.Source: 7th International Symposium on Digital Forensics and Security (ISDFS 2019), Barcelos, Portugal, 10/6/2019, 12/6/2019
DOI: 10.1109/ISDFS.2019.8757511

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2019 Conference object Open Access OPEN

CNN-based system for low resolution face recognition
Massoli F. V., Amato G., Falchi F., Gennaro C., Vairo C.
Since the publication of the AlexNet in 2012, Deep Convolutional Neural Network models became the most promising and powerful technique for image representation. Specifically, the ability of their inner layers to extract high level abstractions of the input images, called deep features vectors, has been employed. Such vectors live in a high dimensional space in which an inner product and thus a metric is defined. The latter allows to carry out similarity measurements among them. This property is particularly useful in order to accomplish tasks such as Face Recognition. Indeed, in order to identify a person it is possible to compare deep features, used as face descriptors, from different identities by means of their similarities. Surveillance systems, among others, utilize this technique. To be precise, deep features extracted from probe images are matched against a database of descriptors from known identities. A critical point is that the database typically contains features extracted from high resolution images while the probes, taken by surveillance cameras, can be at a very low resolution. Therefore, it is mandatory to have a neural network which is able to extract deep features that are robust with respect to resolution variations. In this paper we discuss a CNN-based pipeline that we built for the task of Face Recognition among images with different resolution. The entire system relies on the ability of a CNN to extract deep features that can be used to perform a similarity search in order to fulfill the face recognition task.Source: 27th Italian Symposium on Advanced Database Systems, Castiglione della Pescaia (Grosseto), Italy, June 16th to 19th, 2019

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2019 Conference object Open Access OPEN

Parking Lot Monitoring with Smart Cameras
Amato G., Bolettieri P., Carrara F., Ciampi L., Gennaro C., Leone G. R., Moroni D., Pieri G., Vairo C.
In this article, we present a scenario for monitoring the occupancy of parking spaces in the historical city of Lucca (Italy) based on the use of intelligent cameras and the most modern technologies of artificial intelligence. The system is designed to use different smart-camera prototypes: where the connection to the power grid is available, we propose a powerful embedded hardware solution that exploits a Deep Neural Network. Otherwise, a fully autonomous energy-harvesting node based on a low-energy custom board employing lightweight image analysis algorithms is considered.Source: 5th Italian Conference on ICT for Smart Cities And Communities, pp. 1–3, Pisa, Italy, 18-20 September, 2019

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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.

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2018 Part of book or chapter of book Open Access OPEN

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: A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, edited by Sergio Flesca, Sergio Greco, Elio Masciari, Domenico Saccà, pp. 287–306, 2018
DOI: 10.1007/978-3-319-61893-7_17

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2018 Article Open Access OPEN

Towards multimodal surveillance for smart building security
Amato G., Barsocchi P., Falchi F., Ferro E., Gennaro C., Leone G. R., Moroni D., Salvetti O., Vairo C.
The main goal of a surveillance system is to collect information in a sensing environment and notify unexpected behavior. Information provided by single sensor and surveillance technology may not be sufficient to understand the whole context of the monitored environment. On the other hand, by combining information coming from different sources, the overall performance of a surveillance system can be improved. In this paper, we present the Smart Building Suite, in which independent and different technologies are developed in order to realize a multimodal surveillance system.Source: Proceedings (MDPI) 2 (2018). doi:10.3390/proceedings2020095
DOI: 10.3390/proceedings2020095

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2018 Conference object Open Access OPEN

Smart farming: opportunities, challenges and technology enablers
Bacco F. M., Berton A., Ferro E., Gennaro C., Gotta A., Matteoli S., Paonessa F., Ruggeri M., Virone G., Zanella A.
Agriculture is taking advantage of the Internet of Things paradigm and of the use of autonomous vehicles. The 21st century farm will be run by interconnected vehicles: an enormous potential can be provided by the integration of different technologies to achieve automated operations requiring minimum supervision. This work surveys the most relevant use cases in this field and the available communication technologies, highlighting how connectivity requirements can be met with already available technologies or upcoming standards. Intelligence is considered as a further enabler of automated operations, and this work provides examples of its uses.Source: IoT Vertical and Topical Summit for Agriculture, pp. 1–6, Borgo San Luigi in Monteriggioni, Siena, Italy, 8-9 May 2018
DOI: 10.1109/IOT-TUSCANY.2018.8373043

See at: ISTI Repository Open Access | DOI Resolver | ieeexplore.ieee.org | CNR People