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2021 Article Open Access OPEN

Solving the same-different task with convolutional neural networks
Messina N., Amato G. Carrara F., Gennaro C., Falchi F.
Deep learning demonstrated major abilities in solving many kinds of different real-world problems in computer vision literature. However, they are still strained by simple reasoning tasks that humans consider easy to solve. In this work, we probe current state-of-the-art convolutional neural networks on a difficult set of tasks known as the same-different problems. All the problems require the same prerequisite to be solved correctly: understanding if two random shapes inside the same image are the same or not. With the experiments carried out in this work, we demonstrate that residual connections, and more generally the skip connections, seem to have only a marginal impact on the learning of the proposed problems. In particular, we experiment with DenseNets, and we examine the contribution of residual and recurrent connections in already tested architectures, ResNet-18, and CorNet-S respectively. Our experiments show that older feed-forward networks, AlexNet and VGG, are almost unable to learn the proposed problems, except in some specific scenarios. We show that recently introduced architectures can converge even in the cases where the important parts of their architecture are removed. We finally carry out some zero-shot generalization tests, and we discover that in these scenarios residual and recurrent connections can have a stronger impact on the overall test accuracy. On four difficult problems from the SVRT dataset, we can reach state-of-the-art results with respect to the previous approaches, obtaining super-human performances on three of the four problems.Source: Pattern recognition letters 143 (2021): 75–80. doi:10.1016/j.patrec.2020.12.019
DOI: 10.1016/j.patrec.2020.12.019
Project(s): AI4EU via OpenAIRE

See at: arXiv.org e-Print Archive Open Access | CNR ExploRA Restricted | www.sciencedirect.com Restricted


2021 Conference object Open Access OPEN

Domain adaptation for traffic density estimation
Ciampi L., Santiago C., Costeira J. P., Gennaro C., Amato G.
Convolutional Neural Networks have produced state-of-the-art results for a multitude of computer vision tasks under supervised learning. However, the crux of these methods is the need for a massive amount of labeled data to guarantee that they generalize well to diverse testing scenarios. In many real-world applications, there is indeed a large domain shift between the distributions of the train (source) and test (target) domains, leading to a significant drop in performance at inference time. Unsupervised Domain Adaptation (UDA) is a class of techniques that aims to mitigate this drawback without the need for labeled data in the target domain. This makes it particularly useful for the tasks in which acquiring new labeled data is very expensive, such as for semantic and instance segmentation. In this work, we propose an end-to-end CNN-based UDA algorithm for traffic density estimation and counting, based on adversarial learning in the output space. The density estimation is one of those tasks requiring per-pixel annotated labels and, therefore, needs a lot of human effort. We conduct experiments considering different types of domain shifts, and we make publicly available two new datasets for the vehicle counting task that were also used for our tests. One of them, the Grand Traffic Auto dataset, is a synthetic collection of images, obtained using the graphical engine of the Grand Theft Auto video game, automatically annotated with precise per-pixel labels. Experiments show a significant improvement using our UDA algorithm compared to the model's performance without domain adaptation.Source: VISIGRAPP 2021 - 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 185–195, Online Conference, 08-10 February, 2021
DOI: 10.5220/0010303401850195
Project(s): AI4EU via OpenAIRE, AI4Media via OpenAIRE

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


2020 Conference object Open Access OPEN

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: 28th Symposium on Advanced Database Systems (SEBD), pp. 278–285, Villasimius, Sardinia, Italy, 21-24/06/2020

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


2020 Conference object Open Access OPEN

Multi-Resolution Face Recognition with Drones
Amato G., Falchi F., Gennaro C., Massoli F. V., Vairo C.
Smart cameras have recently seen a large diffusion and represent a low-cost solution for improving public security in many scenarios. Moreover, they are light enough to be lifted by a drone. Face recognition enabled by drones equipped with smart cameras has already been reported in the literature. However, the use of the drone generally imposes tighter constraints than other facial recognition scenarios. First, weather conditions, such as the presence of wind, pose a severe limit on image stability. Moreover, the distance the drones fly is typically much high than fixed ground cameras, which inevitably translates into a degraded resolution of the face images. Furthermore, the drones' operational altitudes usually require the use of optical zoom, thus amplifying the harmful effects of their movements. For all these reasons, in drone scenarios, image degradation strongly affects the behavior of face detection and recognition systems. In this work, we studied the performance of deep neural networks for face re-identification specifically designed for low-quality images and applied them to a drone scenario using a publicly available dataset known as DroneSURF.Source: 3rd International Conference on Sensors, Signal and Image Processing, pp. 13–18, Praga, Czech Republic (Virtual), 23-25/10/2020
DOI: 10.1145/3441233.3441237

See at: ISTI Repository Open Access | dl.acm.org Restricted | CNR ExploRA Restricted


2020 Conference object Open Access OPEN

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: 28th Italian Symposium on Advanced Database Systems, pp. 258–265, Virtual (online) due COVID-19, 21-24/06/2020

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


2020 Article Open Access OPEN

5G-Enabled Security Scenarios for Unmanned Aircraft: Experimentation in Urban Environment
Ferro E., Gennaro C., Nordio A., Paonessa F., Vairo C., Virone G., Argentieri A., Berton A., Bragagnini A.
The telecommunication industry has seen rapid growth in the last few decades. This trend has been fostered by the diffusion of wireless communication technologies. In the city of Matera, Italy (European capital of culture 2019), two applications of 5G for public security have been tested by using an aerial drone: the recognition of objects and people in a crowded city and the detection of radio-frequency jammers. This article describes the experiments and the results obtained.Source: Drones volume 4 (2020). doi:10.3390/drones4020022
DOI: 10.3390/drones4020022

See at: Drones Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | DOAJ-Articles Open Access | Drones Open Access


2020 Article Open Access OPEN

Re-ranking via local embeddings: A use case with permutation-based indexing and the nSimplex projection
Vadicamo L., Gennaro C., Falchi F., Chavez E., Connor R., Amato G.
Approximate Nearest Neighbor (ANN) search is a prevalent paradigm for searching intrinsically high dimensional objects in large-scale data sets. Recently, the permutation-based approach for ANN has attracted a lot of interest due to its versatility in being used in the more general class of metric spaces. In this approach, the entire database is ranked by a permutation distance to the query. Typically, permutations allow the efficient selection of a candidate set of results, but typically to achieve high recall or precision this set has to be reviewed using the original metric and data. This can lead to a sizeable percentage of the database being recalled, along with many expensive distance calculations. To reduce the number of metric computations and the number of database elements accessed, we propose here a re-ranking based on a local embedding using the nSimplex projection. The nSimplex projection produces Euclidean vectors from objects in metric spaces which possess the n-point property. The mapping is obtained from the distances to a set of reference objects, and the original metric can be lower bounded and upper bounded by the Euclidean distance of objects sharing the same set of references. Our approach is particularly advantageous for extensive databases or expensive metric function. We reuse the distances computed in the permutations in the first stage, and hence the memory footprint of the index is not increased. An extensive experimental evaluation of our approach is presented, demonstrating excellent results even on a set of hundreds of millions of objects.Source: Information systems (Oxf.) (2020). doi:10.1016/j.is.2020.101506
DOI: 10.1016/j.is.2020.101506
Project(s): AI4EU via OpenAIRE

See at: ISTI Repository Open Access | Information Systems Restricted | Information Systems Restricted | Information Systems Restricted | Information Systems Restricted | Information Systems Restricted | CNR ExploRA Restricted | Information Systems Restricted


2020 Conference object Open Access OPEN

Continuous ODE-defined image features for adaptive retrieval
Carrara F., Amato G., Falchi F., Gennaro C.
In the last years, content-based image retrieval largely benefited from representation extracted from deeper and more complex convolutional neural networks, which became more effective but also more computationally demanding. Despite existing hardware acceleration, query processing times may be easily saturated by deep feature extraction in high-throughput or real-time embedded scenarios, and usually, a trade-off between efficiency and effectiveness has to be accepted. In this work, we experiment with the recently proposed continuous neural networks defined by parametric ordinary differential equations, dubbed ODE-Nets, for adaptive extraction of image representations. Given the continuous evolution of the network hidden state, we propose to approximate the exact feature extraction by taking a previous "near-in-time" hidden state as features with a reduced computational cost. To understand the potential and the limits of this approach, we also evaluate an ODE-only architecture in which we minimize the number of classical layers in order to delegate most of the representation learning process - - and thus the feature extraction process - - to the continuous part of the model. Preliminary experiments on standard benchmarks show that we are able to dynamically control the trade-off between efficiency and effectiveness of feature extraction at inference-time by controlling the evolution of the continuous hidden state. Although ODE-only networks provide the best fine-grained control on the effectiveness-efficiency trade-off, we observed that mixed architectures perform better or comparably to standard residual nets in both the image classification and retrieval setups while using fewer parameters and retaining the controllability of the trade-off.Source: ICMR '20 - International Conference on Multimedia Retrieval, pp. 198–206, Dublin, Ireland, 8-11 June, 2020
DOI: 10.1145/3372278.3390690
Project(s): AI4EU via OpenAIRE

See at: Unknown Repository Open Access | ISTI Repository Open Access | Unknown Repository Restricted | Unknown Repository Restricted | dl.acm.org Restricted | Unknown Repository Restricted | CNR ExploRA Restricted


2020 Conference object Open Access OPEN

Unsupervised vehicle counting via multiple camera domain adaptation
Ciampi L., Santiago C., Costeira J. P., Gennaro C., Amato G.
Monitoring vehicle flow in cities is a crucial issue to improve the urban environment and quality of life of citizens. Images are the best sensing modality to perceive and asses the flow of vehicles in large areas. Current technologies for vehicle counting in images hinge on large quantities of annotated data, preventing their scalability to city-scale as new cameras are added to the system. This is a recurrent problem when dealing with physical systems and a key research area in Machine Learning and AI. We propose and discuss a new methodology to design image-based vehicle density estimators with few labeled data via multiple camera domain adaptations.Source: ECAI-2020 - 1st International Workshop on New Foundations for Human-Centered AI (NeHuAI), pp. 1–4, Online Conference, 04 September, 2020
Project(s): AI4EU via OpenAIRE

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


2020 Article Open Access OPEN

Virtual to real adaptation of pedestrian detectors
Ciampi L., Messina N., Falchi F., Gennaro C., Amato G.
Pedestrian detection through Computer Vision is a building block for a multitude of applications. Recently, there has been an increasing interest in convolutional neural network-based architectures to execute such a task. One of these supervised networks' critical goals is to generalize the knowledge learned during the training phase to new scenarios with different characteristics. A suitably labeled dataset is essential to achieve this purpose. The main problem is that manually annotating a dataset usually requires a lot of human effort, and it is costly. To this end, we introduce ViPeD (Virtual Pedestrian Dataset), a new synthetically generated set of images collected with the highly photo-realistic graphical engine of the video game GTA V (Grand Theft Auto V), where annotations are automatically acquired. However, when training solely on the synthetic dataset, the model experiences a Synthetic2Real domain shift leading to a performance drop when applied to real-world images. To mitigate this gap, we propose two different domain adaptation techniques suitable for the pedestrian detection task, but possibly applicable to general object detection. Experiments show that the network trained with ViPeD can generalize over unseen real-world scenarios better than the detector trained over real-world data, exploiting the variety of our synthetic dataset. Furthermore, we demonstrate that with our domain adaptation techniques, we can reduce the Synthetic2Real domain shift, making the two domains closer and obtaining a performance improvement when testing the network over the real-world images.Source: Sensors (Basel) 20 (2020). doi:10.3390/s20185250
DOI: 10.3390/s20185250

See at: Sensors Open Access | Sensors Open Access | Sensors Open Access | Sensors Open Access | Europe PubMed Central Open Access | Sensors Open Access | Sensors Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Sensors Open Access | Sensors Open Access


2020 Article Open Access OPEN

Learning accurate personal protective equipment detection from virtual worlds
Di Benedetto M., Carrara F., Meloni E., Amato G., Falchi F., Gennaro C.
Deep learning has achieved impressive results in many machine learning tasks such as image recognition and computer vision. Its applicability to supervised problems is however constrained by the availability of high-quality training data consisting of large numbers of humans annotated examples (e.g. millions). To overcome this problem, recently, the AI world is increasingly exploiting artificially generated images or video sequences using realistic photo rendering engines such as those used in entertainment applications. In this way, large sets of training images can be easily created to train deep learning algorithms. In this paper, we generated photo-realistic synthetic image sets to train deep learning models to recognize the correct use of personal safety equipment (e.g., worker safety helmets, high visibility vests, ear protection devices) during at-risk work activities. Then, we performed the adaptation of the domain to real-world images using a very small set of real-world images. We demonstrated that training with the synthetic training set generated and the use of the domain adaptation phase is an effective solution for applications where no training set is available.Source: Multimedia tools and applications (2020). doi:10.1007/s11042-020-09597-9
DOI: 10.1007/s11042-020-09597-9
Project(s): AI4EU via OpenAIRE

See at: ISTI Repository Open Access | Multimedia Tools and Applications Restricted | Multimedia Tools and Applications Restricted | Multimedia Tools and Applications Restricted | Multimedia Tools and Applications Restricted | CNR ExploRA Restricted


2020 Conference object Open Access OPEN

Learning distance estimators from pivoted embeddings of metric objects
Carrara F., Gennaro C., Falchi F., Amato G.
Efficient indexing and retrieval in generic metric spaces often translate into the search for approximate methods that can retrieve relevant samples to a query performing the least amount of distance computations. To this end, when indexing and fulfilling queries, distances are computed and stored only against a small set of reference points (also referred to as pivots) and then adopted in geometrical rules to estimate real distances and include or exclude elements from the result set. In this paper, we propose to learn a regression model that estimates the distance between a pair of metric objects starting from their distances to a set of reference objects. We explore architectural hyper-parameters and compare with the state-of-the-art geometrical method based on the n-simplex projection. Preliminary results show that our model provides a comparable or slightly degraded performance while being more efficient and applicable to generic metric spaces.Source: SISAP 2020: the 13th International Conference on Similarity Search and Applications, pp. 361–368, Copenhagen, Denmark (Virtual), 30/09/2020 - 02/10/2020
DOI: 10.1007/978-3-030-60936-8_28
Project(s): AI4EU via OpenAIRE

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


2020 Report Open Access OPEN

5G: Scenari di monitoraggio attraverso droni. TEST OPERATIVI e DEMO MATERA, ex-ospedale di San Rocco
Ferro E., Gennaro C., Vairo C., Berton A., Virone G., Paonessa F., Argentieri A.
Questo Documento ha lo scopo di descrivere in dettaglio i test operativi fatti a Matera con il drone e la demo fatta in data 27 Giugno davanti a persone del MISE. La localita? usata sia per i test operativi che per la demo è l' ex-ospedale di San Rocco. In particolare, gli scenari oggetto della demo sono: Scenario 8.3.6 - Sicurezza pubblica attraverso l'uso di droni Scenario 8.3.7 - Rilevazione di Jammer a Radiofrequenza mediante Drone I test operativi sono stati effettuati nei giorni 5 e 6 Giugno 2019; in particolare: o 5 Giugno: test dell'intero sistema di comunicazione 5G o 6 Giugno: prove di volo con carico La demo si è svolta Giovedì 27 Giugno 2019.

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


2020 Report Open Access OPEN

5G: Scenari di monitoraggio attraverso droni - D3 - L' uso dei droni nell' agricoltura di precisione a Matera
Ferro E., Gennaro C., Vairo C., Berton A., Argentieri A.
Questo documento ha lo scopo di descrivere i test operativi fatti nel 2020 a Matera con il drone per lo Scenario 8.11.2: Agricoltura di Precisione con Veicoli Autonomi. I test operativi sono stati effettuati compatibilmente con le restrizioni dovute alla pandemia da Covid- 19. L'area interessata (Figura 1) e? stata un campo di trifoglio messo a disposizione da Masseria del Parco (La Martella- Matera- Basilicata), dove l'Universita? della Basilicata, a febbraio 2020 aveva provveduto a fare una fertilizzazione a rateo variabile (da 0,35 a 50kg per ettaro).

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2020 Report Open Access OPEN

AIMH research activities 2020
Aloia N., Amato G., Bartalesi 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 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.

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2020 Conference object Open Access OPEN

Cross-resolution deep features based image search
Massoli F. V., Falchi F., Gennaro C., Amato G.
Deep Learning models proved to be able to generate highly discriminative image descriptors, named deep features, suitable for similarity search tasks such as Person Re-Identification and Image Retrieval. Typically, these models are trained by employing high-resolution datasets, therefore reducing the reliability of the produced representations when low-resolution images are involved. The similarity search task becomes even more challenging in the cross-resolution scenarios, i.e., when a low-resolution query image has to be matched against a database containing descriptors generated from images at different, and usually high, resolutions. To solve this issue, we proposed a deep learning-based approach by which we empowered a ResNet-like architecture to generate resolution-robust deep features. Once trained, our models were able to generate image descriptors less brittle to resolution variations, thus being useful to fulfill a similarity search task in cross-resolution scenarios. To asses their performance, we used synthetic as well as natural low-resolution images. An immediate advantage of our approach is that there is no need for Super-Resolution techniques, thus avoiding the need to synthesize queries at higher resolutions.Source: Similarity Search and Applications, pp. 352–360, Copenhagen, Denmark, 20/09/2020, 2/10/2020
DOI: 10.1007/978-3-030-60936-8_27

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


2020 Conference object Open Access OPEN

Monitoring Traffic Flows via Unsupervised Domain Adaptation
Ciampi L., Gennaro C., Amato G.
Monitoring traffic flows in cities is crucial to improve urban mobility, and images are the best sensing modality to perceive and assess the flow of vehicles in large areas. However, current machine learning-based technologies using images hinge on large quantities of annotated data, preventing their scalability to city-scale as new cameras are added to the system. We propose a new methodology to design image-based vehicle density estimators with few labeled data via an unsupervised domain adaptation technique.Source: 6th Italian Conference on ICT for Smart Cities And Communities, pp. 1–2, Online Conference, 23-25/09/2020,
Project(s): AI4EU via OpenAIRE

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


2019 Article Open Access OPEN

Distributed video surveillance using smart cameras
Kavalionak H., Gennaro C., Amato G., Vairo C., Perciante C., Meghini C., Falchi F.
Video surveillance systems have become an indispensable tool for the security and organization of public and private areas. Most of the current commercial video surveillance systems rely on a classical client/server architecture to perform face and object recognition. In order to support the more complex and advanced video surveillance systems proposed in the last years, companies are required to invest resources in order to maintain the servers dedicated to the recognition tasks. In this work, we propose a novel distributed protocol for a face recognition system that exploits the computational capabilities of the surveillance devices (i.e. cameras) to perform the recognition of the person. The cameras fall back to a centralized server if their hardware capabilities are not enough to perform the recognition. In order to evaluate the proposed algorithm we simulate and test the 1NN and weighted kNN classification algorithms via extensive experiments on a freely available dataset. As a prototype of surveillance devices we have considered Raspberry PI entities. By means of simulations, we show that our algorithm is able to reduce up to 50% of the load from the server with no negative impact on the quality of the surveillance service.Source: Journal of grid computing 17 (2019): 59–77. doi:10.1007/s10723-018-9467-x
DOI: 10.1007/s10723-018-9467-x

See at: ISTI Repository Open Access | Journal of Grid Computing Restricted | Journal of Grid Computing Restricted | Journal of Grid Computing Restricted | link.springer.com Restricted | Journal of Grid Computing Restricted | Journal of Grid Computing Restricted | Journal of Grid Computing Restricted | CNR ExploRA Restricted


2019 Conference object Open Access OPEN

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.Source: International Conference on Similarity Search and Applications, pp. 40–48, Newark, NJ, USA, 2-4/10/2019
DOI: 10.1007/978-3-030-32047-8_4

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


2019 Conference object 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 | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | CNR ExploRA Restricted | Unknown Repository Restricted