We present an image search engine that allows searching by similarity about 100M images included in the YFCC100M dataset, and annotate query images. Image similarity search is performed using YFCC100M-HNfc6, the set of deep features we... more
We present an image search engine that allows searching by similarity about 100M images included in the YFCC100M dataset, and annotate query images. Image similarity search is performed using YFCC100M-HNfc6, the set of deep features we extracted from the YFCC100M dataset, which was indexed using the MI-File index for efficient similarity searching. A metadata cleaning algorithm, that uses visual and textual analysis, was used to select from the YFCC100M dataset a relevant subset of images and associated annotations, to create a training set to perform automatic textual annotation of submitted queries. The on-line image and annotation system demonstrates the effectiveness of the deep features for assessing conceptual similarity among images, the effectiveness of the metadata cleaning algorithm, to identify a relevant training set for annotation, and the efficiency and accuracy of the MI-File similarity index techniques, to search and annotate using a dataset of 100M images, with very limited computing resources.
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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... more
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.
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Face verification is a key task in many application fields, such as security and surveillance. Several approaches and methodologies are currently used to try to determine if two faces belong to the same person. Among these, facial... more
Face verification is a key task in many application fields, such as security and surveillance. Several approaches and methodologies are currently used to try to determine if two faces belong to the same person. Among these, facial landmarks are very important in forensics, since the distance between some characteristic points of a face can be used as an objective measure in court during trials. However, the accuracy of the approaches based on facial landmarks in verifying whether a face belongs to a given person or not is often not quite good. Recently, deep learning approaches have been proposed to address the face verification problem, with very good results. In this paper, we compare the accuracy of facial landmarks and deep learning approaches in performing the face verification task. Our experiments, conducted on a real case scenario, show that the deep learning approach greatly outperforms in accuracy the facial landmarks approach. Keywords–Face Verification; Facial Landmarks;...
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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... more
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.
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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 recognition system that takes advantage... more
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 recognition 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 capabilities 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 Raspberry 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.
Research Interests: Computer Science and SEBD
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. This paper presents an approach to transform neural network... more
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. 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 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. An extensive experimental evaluation conducted on standard benchmarks shows the effectiveness and efficiency of the proposed approach and how it compares to state-of-the-art main-memory indexes.
Research Interests: Computer Science and SEBD
In this paper we present a Wireless Sensor Network (WSN), which is intended to provide a scalable solution for active cooperative monitoring of wide geographical areas. The system is designed to use different smart-camera prototypes:... more
In this paper we present a Wireless Sensor Network (WSN), which is intended to provide a scalable solution for active cooperative monitoring of wide geographical areas. The system is designed to use different smart-camera prototypes: where the connection to the power grid is available a powerful embedded hardware implements a Deep Neural Network, otherwise a fully autonomous energy-harvesting node based on a low-energy custom board employs lightweight image analysis algorithms. Parking lots occupancy monitoring in the historical city of Lucca (Italy) is the application where the implemented smart cameras have been deployed. Traffic monitoring and surveillance are possible new scenarios for the system.
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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... more
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.
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We propose an effective CNN architecture for visual parking occupancy detection.The CNN architecture is small enough to run on smart cameras.The proposed solution performs and generalizes better th...
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This paper presents an approach for real-time car parking occupancy detection that uses a Convolutional Neural Network (CNN) classifier running on-board of a smart camera with limited resources. Experiments show that our technique is very... more
This paper presents an approach for real-time car parking occupancy detection that uses a Convolutional Neural Network (CNN) classifier running on-board of a smart camera with limited resources. Experiments show that our technique is very effective and robust to light condition changes, presence of shadows, and partial occlusions. The detection is reliable, even when tests are performed using images captured from a viewpoint different than the viewpoint used for training. In addition, it also demonstrates its robustness when training and tests are executed on different parking lots. We have tested and compared our solution against state of the art techniques, using a reference benchmark for parking occupancy detection. We have also produced and made publicly available an additional dataset that contains images of the parking lot taken from different viewpoints and in different days with different light conditions. The dataset captures occlusion and shadows that might disturb the classification of the parking spaces status.
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In recent years, Quantum Computing witnessed massive improvements in terms of available resources and algorithms development. The ability to harness quantum phenomena to solve computational problems is a long-standing dream that has drawn... more
In recent years, Quantum Computing witnessed massive improvements in terms of available resources and algorithms development. The ability to harness quantum phenomena to solve computational problems is a long-standing dream that has drawn the scientific community’s interest since the late 80s. In such a context, we propose our contribution. First, we introduce basic concepts related to quantum computations, and then we explain the core functionalities of technologies that implement the Gate Model and Adiabatic Quantum Computing paradigms. Finally, we gather, compare and analyze the current state-of-the-art concerning Quantum Perceptrons and Quantum Neural Networks implementations.
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Content-based image classification is a wide research field that addresses the landmark recognition problem. Among the many classification techniques proposed, the k -nearest neighbor ( kNN ) is one of the most simple and widely used... more
Content-based image classification is a wide research field that addresses the landmark recognition problem. Among the many classification techniques proposed, the k -nearest neighbor ( kNN ) is one of the most simple and widely used methods. In this article, we use kNN classification and landmark recognition techniques to address the problem of monument recognition in images. We propose two novel approaches that exploit kNN classification technique in conjunction with local visual descriptors. The first approach is based on a relaxed definition of the local feature based image to image similarity and allows standard kNN classification to be efficiently executed with the support of access methods for similarity search. The second approach uses kNN classification to classify local features rather than images. An image is classified evaluating the consensus among the classification of its local features. In this case, access methods for similarity search can be used to make the classi...
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Abstract Content-based image retrieval (CBIR for short) methods aim at capturing image similarity by relying on some specific characteristic of images such as color, texture and shape. The model we propose addresses the problem of... more
Abstract Content-based image retrieval (CBIR for short) methods aim at capturing image similarity by relying on some specific characteristic of images such as color, texture and shape. The model we propose addresses the problem of exploring the image space ...
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Content-based image retrieval is becoming a popular way for searching digital libraries as the amount of available multimedia data increases. However, the cost of developing from scratch a robust and reliable system with content-based... more
Content-based image retrieval is becoming a popular way for searching digital libraries as the amount of available multimedia data increases. However, the cost of developing from scratch a robust and reliable system with content-based image retrieval facilities for large databases is quite prohibitive. In this paper, we propose to exploit an approach to perform approximate similarity search in metric spaces developed by [3, 6]. The idea at the basis of these techniques is that when two objects are very close one to each other they'see'the ...
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Similarity search technique has been proved to be an effective way for retrieving multimedia content. However, as the amount of available multimedia data increases, the cost of developing from scratch a robust and scalable system with... more
Similarity search technique has been proved to be an effective way for retrieving multimedia content. However, as the amount of available multimedia data increases, the cost of developing from scratch a robust and scalable system with content-based image retrieval facilities is quite prohibitive.
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We present the VIsual Support to Interactive TOurism in Tuscany (VISITO Tuscany) project which offers an interactive guide for tourists visiting cities of art accessible via smartphones. The peculiarity of the system is that user... more
We present the VIsual Support to Interactive TOurism in Tuscany (VISITO Tuscany) project which offers an interactive guide for tourists visiting cities of art accessible via smartphones. The peculiarity of the system is that user interaction is mainly obtained by the use of images -- In order to receive information on a particular monument users just have to take a picture of it. VISITO Tuscany, using techniques of image analysis and content recognition, automatically recognize the photographed monuments and pertinent information is displayed to the user. In this paper we illustrate how the use of landmarks recognition from mobile devices can provide the tourist with relevant and customized information about various type of objects in cities of art.
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... Status: Published. Glasgow Author(s): Innocenti, Ms Perla. Authors: Innocenti, P. Subjects: Z Bibliography. Library Science. ... Unique ID: glaseprints:2004-52809. Depositing User: Ms PerlaInnocenti. Deposited On: 15 Jun 2011 14:48.... more
... Status: Published. Glasgow Author(s): Innocenti, Ms Perla. Authors: Innocenti, P. Subjects: Z Bibliography. Library Science. ... Unique ID: glaseprints:2004-52809. Depositing User: Ms PerlaInnocenti. Deposited On: 15 Jun 2011 14:48. Last Modified: 15 Jun 2011 14:48. ...
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In this paper we present the MILOS 1 Multimedia Content Management System. MILOS supports the storage and content based retrieval of any multimedia documents whose descriptions are provided by using arbitrary metadata models represented... more
In this paper we present the MILOS 1 Multimedia Content Management System. MILOS supports the storage and content based retrieval of any multimedia documents whose descriptions are provided by using arbitrary metadata models represented in XML. It provides developers of digital library applications with functionalities for dealing with heterogeneous digital documents, heterogeneous metadata, and metadata schema mapping.
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In this paper we present a prototype system to enrich audiovisual contents with annotations, which exploits existing technologies for automatic extraction of metadata (such as OCR, speech recognition, cut detection, visual descriptors,... more
In this paper we present a prototype system to enrich audiovisual contents with annotations, which exploits existing technologies for automatic extraction of metadata (such as OCR, speech recognition, cut detection, visual descriptors, etc.). The prototype relies on a metadata model that unifies MPEG-7 and LOM descriptions to edit and enrich audiovisual contents, and it is based on MILOS, a general purpose Multimedia Content Management System created to support design and effective implementation of digital library ...
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In this communication a representation of the links between DNA-relatives based on Graph Theory is applied to the analysis of personal genomic data to obtain genealogical information. The method is tested on both simulated and real data... more
In this communication a representation of the links between DNA-relatives based on Graph Theory is applied to the analysis of personal genomic data to obtain genealogical information. The method is tested on both simulated and real data and its applicability to the field of genealogical research is discussed. We envisage the proposed approach as a valid tool for a streamlined application to the publicly available data generated by many online personal genomic companies. In this way, anonymized matrices of pairwise genome sharing counts can help to improve the retrieval of genetic relationships between customers who provide explicit consent to the treatment of their data.