@article {aPinaa, title = {Layer-Wise Training of Graph Neural Networks With Self-Supervised Learning}, journal = { IEEE Transactions on Neural Networks and Learning Systems,}, year = {2025}, month = {07/2025}, doi = {10.1109/TNNLS.2025.3577702}, author = {Oscar Pina and Ver{\'o}nica Vilaplana} } @conference {cLo Anguera25, title = {Leveraging Quantum Machine Learning for Intrusion Detection in Software-Defined Networks}, booktitle = {IEEE International Conference on Machine Learning for Communication and Networking}, year = {2025}, month = {05/2025}, publisher = {IEEE}, organization = {IEEE}, address = {Barcelona}, abstract = {

Quantum machine learning (QML) algorithms\ for intrusion detection systems in software-defined networks are investigated, and their effectiveness is compared with their classical machine learning methods. The University of Nevada - Reno intrusion detection dataset (UNR-IDD) is used to evaluate different QML models, including quantum k-nearest neighbors (QKNNs), quantum support vector machines (QSVMs), quantum neural networks (QNNs), and hybrid quantum neural networks (HQNNs). These models were tested with quantum simulators to evaluate their potential advantages in processing complex datasets. The results show that HQNN and QSVM have higher accuracy than their classical SVM and NN counterparts. This study shows the potential of leveraging QML to enhance precision. References to other works that dive into efficiency and complexity are included.

}, keywords = {Cybersecurity, Intrusion Detection, Quantum machine learning (QML), Software-Defined Networks (SDN)}, doi = {10.5281/zenodo.15784800}, url = {https://icmlcn2025.ieee-icmlcn.org/program/special-sessions}, author = {Joan Lo Anguera and Ruiz-Hidalgo, J. and Sol{\'e}, A. and Casas, J. and Jos{\'e} Antonio L{\'a}zaro and Samuel Sarmiento and Adolfo Ler{\'\i}n} } @conference {ca, title = {Lossless and Lossy Compression for Distributed Acoustic Sensing Using Inter-channel Predictions}, booktitle = {SSA Photonic Seismology 2024}, year = {2024}, month = {10/2024}, address = {Vancouver}, abstract = {

Distributed Acoustic Sensing (DAS) offer unique advantages to extensive monitoring initiatives due to its long-distance, high-density arrays of real-time acoustic sensors. However, high-resolution and long-term applications generate a large amount of data, on the order of hundreds of terabytes per year, presenting challenges for processing and storage. As a result, leveraging innovative data compression techniques is essential for scaling up DAS monitoring. DAS arrays are composed of multiple channels carrying highly correlated and coherent signals. These redundancies allow to exploit inter-channel compression, which use predictions from consecutive channel signals and achieve a higher compression ratio compared to compressing each channel separately.

In this work, we propose a novel coding scheme for DAS data that improves state-of-the-art compression. Encoding follows a pipeline composed of intra-prediction, inter-prediction, transform and entropy coding. For lowly correlated channels, intra-channel prediction is realized by means of Linear Prediction Coding. For inter-channel prediction, we propose three different methods: (1) prediction by warping and scaling the signal from the consecutive channel, (2) a linear predictor is learned over many channels and (3) wavelet transforms are used to disentangle data signals of different frequency, allowing fine-grained prediction. Entropy coding is done by a combination of adaptive arithmetic coding and run-length coding. The implementation is divided into an encoder and a decoder. The encoder uses bitrate optimization for decisions on the prediction methods and parameter values and can be tuned for either speed or high-compression modes. In addition, we explore lossy quantization by adding tunable quantization of the transformed signal, which we observe to achieve significantly higher compression at the expense of quantization noise. The designed algorithms and the provided implementation facilitate the deployment of long-term DAS monitoring.

}, keywords = {Distributed Acoustic Sensing, Signal compression}, url = {https://ssafallmeeting.secure-platform.com/a/gallery/rounds/39/details/12071}, author = {Segu{\'\i}, Aleix and Ugalde, Arantza and Ventosa, Sergi and Morros, J.R.} } @conference {cPinab, title = {Layer-wise self-supervised learning on graphs}, booktitle = {KDD 2023 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD 2023)}, year = {2023}, month = {08/2023}, address = {Long Beach, USA}, abstract = {

End-to-end training of graph neural networks (GNN) on large graphs presents several memory and computational challenges, and limits the application to shallow architectures as depth exponentially increases the memory and space complexities. In this manuscript, we propose Layer-wise Regularized Graph Infomax, an algorithm to train GNNs layer by layer in a self-supervised manner. We decouple the feature propagation and feature transformation carried out by GNNs to learn node representations in order to derive a loss function based on the prediction of future inputs. We evaluate the algorithm in inductive large graphs and show similar performance to other end to end methods and a substantially increased efficiency, which enables the training of more sophisticated models in one single device. We also show that our algorithm avoids the oversmoothing of the representations, another common challenge of deep GNNs.

}, author = {Oscar Pina and Ver{\'o}nica Vilaplana} } @article {aGene-Mola23, title = {Looking behind occlusions: A study on amodal segmentation for robust on-tree apple fruit size estimation}, journal = {Computers and Electronics in Agriculture}, volume = {209}, year = {2023}, month = {04/2023}, abstract = {

The detection and sizing of fruits with computer vision methods is of interest because it provides relevant information to improve the management of orchard farming. However, the presence of partially occluded fruits limits the performance of existing methods, making reliable fruit sizing a challenging task. While previous fruit segmentation works limit segmentation to the visible region of fruits (known as modal segmentation), in this work we propose an amodal segmentation algorithm to predict the complete shape, which includes its visible and occluded regions. To do so, an end-to-end convolutional neural network (CNN) for simultaneous modal and amodal instance segmentation was implemented. The predicted amodal masks were used to estimate the fruit diameters in pixels. Modal masks were used to identify the visible region and measure the distance between the apples and the camera using the depth image. Finally, the fruit diameters in millimetres (mm) were computed by applying the pinhole camera model. The method was developed with a Fuji apple dataset consisting of 3925 RGB-D images acquired at different growth stages with a total of 15,335 annotated apples, and was subsequently tested in a case study to measure the diameter of Elstar apples at different growth stages. Fruit detection results showed an F1-score of 0.86 and the fruit diameter results reported a mean absolute error (MAE) of 4.5\ mm and R2\ =\ 0.80 irrespective of fruit visibility. Besides the diameter estimation, modal and amodal masks were used to automatically determine the percentage of visibility of measured apples. This feature was used as a confidence value, improving the diameter estimation to MAE\ =\ 2.93\ mm and R2\ =\ 0.91 when limiting the size estimation to fruits detected with a visibility higher than 60\%. The main advantages of the present methodology are its robustness for measuring partially occluded fruits and the capability to determine the visibility percentage. The main limitation is that depth images were generated by means of photogrammetry methods, which limits the efficiency of data acquisition. To overcome this limitation, future works should consider the use of commercial RGB-D sensors. The code and the dataset used to evaluate the method have been made publicly available at\ https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.

}, keywords = {deep learning, Fruit detection, Fruit measurement, Fruit visibility, Precision agriculture, Yield estimation}, issn = {ISSN 0168-1699}, doi = {https://doi.org/10.1016/j.compag.2023.107854}, url = {https://authors.elsevier.com/sd/article/S0168-1699(23)00242-9}, author = {Gen{\'e}-Mola, Jordi and Ferrer-Ferrer, M. and Gregorio, Eduard and Blok, P. M. and Hemming, J. and Morros, J.R. and Rosell-Polo, Joan R. and Ver{\'o}nica Vilaplana and Ruiz-Hidalgo, J.} } @article {xGiro-i-Nieto21b, title = {Learning Representations for Sign Language Videos}, year = {2021}, abstract = {

These slides review the research of our lab since 2016 on applied deep learning, starting from our participation in the TRECVID Instance Search 2014, moving into video analysis with CNN+RNN architectures, and our current efforts in sign language translation and production.

Learning Representations for Sign Language Videos - Xavier Giro - NIST TRECVID 2021 from Universitat Polit{\`e}cnica de Catalunya
}, author = {Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xMuschik20, title = {Learn2Sign : sign language recognition and translation using human keypoint estimation and transformer model}, year = {2020}, abstract = {

Sign language recognition and translation has been an active research field in the recent years with most approaches using deep neural networks to extract information from sign language data. This work investigates the mostly disregarded approach of using human keypoint estimation from image and video data with OpenPose in combination with transformer network architecture. Firstly, it was shown that it is possible to recognize individual signs (4.5\% word error rate (WER)). Continuous sign language recognition though was more error prone (77.3\% WER) and sign language translation was not possible using the proposed methods, which might be due to low accuracy scores of human keypoint estimation by OpenPose and accompanying loss of information or insufficient capacities of the used transformer model. Results may improve with the use of datasets containing higher repetition rates of individual signs or focusing more precisely on keypoint extraction of hands.

}, doi = {10.18419/opus-11197}, url = {https://elib.uni-stuttgart.de/handle/11682/11214}, author = {Muschik, Peter}, editor = {A. Duarte and Xavier Gir{\'o}-i-Nieto} } @phdthesis {dPujol-Miro20, title = {Learning to extract features for 2D-3D multimodal registration}, year = {2020}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

The ability to capture depth information form an scene has greatly increased in the recent years. 3D sensors, traditionally high cost and low resolution sensors, are being democratized and 3D scans of indoor and outdoor scenes are becoming more and more common.

However, there is still a great data gap between the amount of captures being per- formed with 2D and 3D sensors. Although the 3D sensors provide more information about the scene, 2D sensors are still more accessible and widely used. This trade-off between availability and information between sensors brings us to a multimodal scenario of mixed 2D and 3D data.

This thesis explores the fundamental block of this multimodal scenario: the reg- istration between a single 2D image and a single unorganized point cloud. An unorganized 3D point cloud is the basic representation of a 3D capture. In this representation the surveyed points are represented only by their real word coordi- nates and, optionally, by their colour information. This simplistic representation brings multiple challenges to the registration, since most of the state of the art works leverage the existence of metadata about the scene or prior knowledges.

Two different techniques are explored to perform the registration: a keypoint-based technique and an edge-based technique. The keypoint-based technique estimates the transformation by means of correspondences detected using Deep Learning, whilst the edge-based technique refines a transformation using a multimodal edge detection to establish anchor points to perform the estimation.

An extensive evaluation of the proposed methodologies is performed. Albeit further research is needed to achieve adequate performances, the obtained results show the potential of the usage of deep learning techniques to learn 2D and 3D similari- ties. The results also show the good performance of the proposed 2D-3D iterative refinement, up to the state of the art on 3D-3D registration.

}, url = {http://hdl.handle.net/2117/330132}, author = {A. Pujol-Mir{\'o}}, editor = {Casas, J. and Ruiz-Hidalgo, J.} } @article {xGiro-i-Nieto18, title = {Learning Where and When to Look}, year = {2018}, abstract = {

Deep learning models do not only achieve superior performances in image recognition tasks, but also in predicting where and when users focus their attention. This talk will provide an overview of how convolutional neural networks have been trained to predict saliency maps that describe the probability of fixing the gaze on each image location. Different solution have been proposed for this task, and our recent work has added a temporal dimension by predicting the gaze scanpath over 360 degree images for VR/AR. These techniques allow simulating eye tracker data with no need of user data collection.

Learning Where and When to Look $\#$reworkRETAIL 2018 from Universitat Polit{\`e}cnica de Catalunya
}, url = {https://www.re-work.co/events/deep-learning-in-retail-summit-london-2018}, author = {Xavier Gir{\'o}-i-Nieto} } @conference {cGorrizb, title = {Leishmaniasis Parasite Segmentation and Classification Using Deep Learning}, booktitle = {International Conference on Articulated Motion and Deformable Objects}, year = {2018}, address = {Palma, Spain}, abstract = {

Leishmaniasis is considered a neglected disease that causes thousands of deaths annually in some tropical and subtropical countries. There are various techniques to diagnose leishmaniasis of which manual microscopy is considered to be the gold standard. There is a need for the development of automatic techniques that are able to detect parasites in a robust and unsupervised manner. In this paper we present a procedure for automatizing the detection process based on a deep learning approach. We train a U-net model that successfully segments leismania parasites and classifies them into promastigotes, amastigotes and adhered parasites.

}, author = {G{\'o}rriz, Marc and Albert Aparicio and Berta Ravent{\'o}s and Daniel L{\'o}pez-Codina and Ver{\'o}nica Vilaplana and Elisa Sayrol} } @inbook {bGorriz18, title = {Leishmaniasis Parasite Segmentation and Classification Using Deep Learning}, booktitle = { Articulated Motion and Deformable Objects}, volume = {10945}, number = {Lecture Notes in Computer Science}, year = {2018}, pages = {53-62}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

Leishmaniasis is considered a neglected disease that causes thousands of deaths annually in some tropical and subtropical countries. There are various techniques to diagnose leishmaniasis of which manual microscopy is considered to be the gold standard. There is a need for the development of automatic techniques that are able to detect parasites in a robust and unsupervised manner. In this paper we present a procedure for automatizing the detection process based on a deep learning approach. We train a U-net model that successfully segments leismania parasites and classifies them into promastigotes, amastigotes and adhered parasites.

}, issn = {978-3-319-94544-6}, doi = {10.1007/978-3-319-94544-6}, author = {G{\'o}rriz, Marc and Albert Aparicio and Berta Ravent{\'o}s and Ver{\'o}nica Vilaplana and Elisa Sayrol and Daniel L{\'o}pez-Codina} } @conference {cFernandezc, title = {Linking Media: adopting Semantic Technologies for multimodal media connection}, booktitle = {International Semantic Web Conference - ISWC (Industry Track)}, year = {2018}, month = {08/2018}, address = {Monterey, CA, USA}, abstract = {

Today{\textquoteright}s media and news organizations are constantly generating large amounts of multimedia content, majorly delivered online. As the online media market grows, the management and delivery of contents is becoming a challenge.\ Computational approaches can help to overcome this challenge by governing different applications such as content creation, production, search, and its promotion and distribution to different audiences.\ In this abstract we present a success story of the adoption of semantic technologies on the aforementioned applications, which\ \ are built on top of a semantic tagging framework, based on a Knowledge Graph (KG).\ The presented pipeline combines multimodal inputs into a contextual entity linking module, which indexes documents and links them to trends and stories developing on the news.\ \ We will describe how documents are linked and provided to media producers through Vilynx{\textquoteright}s platform, which is currently indexing over 20k media documents a day.

}, keywords = {Knowledge graph; Linked data; Multimedia; Semantic web Linked data; Computational approach; Content creation; Knowledge graphs; Multimedia; Multimedia contents; Multimodal inputs; Semantic tagging; Semantic technologies; Semantic Web}, url = {http://ceur-ws.org/Vol-2180/}, author = {Fern{\`a}ndez, D{\`e}lia and Bou-Balust, Elisenda and Xavier Gir{\'o}-i-Nieto} } @article {xGiro-i-Nieto17, title = {La meitat de les not{\'\i}cies que consumirem el 2022 seran falses}, year = {2017}, publisher = {Corporaci{\'o} Catalana de Mitjans Audiovisuals}, address = {Sant Joan Desp{\'\i}}, abstract = {

Reportatge em{\`e}s dins el Telenot{\'\i}cies Vespre de Televisi{\'o} de Catalunya el diumenge 26 de novembre de 2017.

Els programes d{\textquoteright}intel{\textperiodcentered}lig{\`e}ncia artificial s{\'o}n capa{\c c}os de crear imatges i veus cada cop m{\'e}s realistes i obren la porta a generar mentides de forma m{\'e}s automatitzada

}, keywords = {deep learning, fake news, gan}, url = {http://www.ccma.cat/324/la-meitat-de-les-noticies-que-consumirem-el-2022-seran-falses/noticia/2823178/}, author = {Xavier Gir{\'o}-i-Nieto and Pascual-deLaPuente, Santiago and Mir{\'o}, Vict{\`o}ria and Esteve, Oriol} } @conference {cSalvadorc, title = {Learning Cross-modal Embeddings for Cooking Recipes and Food Images}, booktitle = {CVPR}, year = {2017}, month = {03/2017}, publisher = {CVF / IEEE}, organization = {CVF / IEEE}, address = {Honolulu, Hawaii, USA}, abstract = {

In this paper, we introduce Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images. As the largest publicly available collection of recipe data, Recipe1M affords the ability to train high-capacity models on aligned, multi-modal data. Using these data, we train a neural network to find a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Additionally, we demonstrate that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulate that these embeddings will provide a basis for further exploration of the Recipe1M dataset and food and cooking in general.

[Project page]

In the news:

}, doi = {10.1109/CVPR.2017.327}, url = {http://openaccess.thecvf.com/content_cvpr_2017/html/Salvador_Learning_Cross-Modal_Embeddings_CVPR_2017_paper.html}, author = {Amaia Salvador and Hynes, Nicholas and Aytar, Yusuf and Marin, Javier and Ofli, Ferda and Weber, Ingmar and Torralba, Antonio} } @mastersthesis {xCampos17, title = {Learning to Skip State Updates in Recurrent Neural Networks}, year = {2017}, abstract = {

Program:\ Master{\textquoteright}s Degree in Telecommunications Engineering

Grade: A with honours (10.0/10.0)

Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and dificulty in capturing long term dependencies. In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time. We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This network can be encouraged to perform fewer state updates through a novel loss term. We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline models.

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks from Xavier Giro-i-Nieto
}, keywords = {conditional computation, deep learning, machine learning, recurrent neural networks, sequence modeling}, url = {https://imatge-upc.github.io/skiprnn-2017-telecombcn/}, author = {V{\'\i}ctor Campos}, editor = {Jou, Brendan and Chang, Shih-Fu and Xavier Gir{\'o}-i-Nieto} } @conference {cGurrina, title = {LTA 2017: The Second Workshop on Lifelogging Tools and Applications}, booktitle = {ACM Multimedia}, year = {2017}, month = {10/2017}, publisher = {ACM}, organization = {ACM}, address = {Mountain View, California USA}, abstract = {

The organisation of personal data is receiving increasing research attention due to the challenges we face in gathering, enriching, searching, and visualising such data. \ Given the increasing ease with which personal data being gathered by individuals, the concept of a lifelog digital library of rich multimedia and sensory content for every individual is fast becoming a reality. \ The LTA~2017 workshop aims to bring together academics and practitioners to discuss approaches to lifelog data analytics and applications; and to debate the opportunities and challenges for researchers in this new and challenging area.

}, doi = {10.1145/3123266.3132050}, author = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Dimiccoli, M. and Dang-Nguyen, Duc-Tien and Joho, H.} } @conference {cdeOliveira-Barraa, title = {Large Scale Content-Based Video Retrieval with LIvRE}, booktitle = {14th International Workshop on Content-based Multimedia Indexing (CBMI)}, year = {2016}, month = {06/2016}, publisher = {IEEE}, organization = {IEEE}, address = {Bucharest, Romania}, abstract = {

The fast growth of video data requires robust, efficient, and scalable systems to allow for indexing and retrieval. These systems must be accessible from lightweight, portable and usable interfaces to help users in management and search of video content. This demo paper presents LIvRE, an extension of an existing open source tool for image retrieval to support video indexing. LIvRE consists of three main system components (pre-processing, indexing and retrieval), as well as a scalable and responsive HTML5 user interface accessible from a web browser. LIvRE supports image-based queries, which are efficiently matched with the extracted frames of the indexed videos.

}, author = {Gabriel de Oliveira-Barra and Lux, Mathias and Xavier Gir{\'o}-i-Nieto} } @conference {cdeOliveira-Barra, title = {LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task}, booktitle = {The 12th NTCIR Conference, Evaluation of Information Access Technologies}, year = {2016}, month = {06/2016}, publisher = {National Institute of Informatics (NII)}, organization = {National Institute of Informatics (NII)}, address = {Tokyo, Japan}, abstract = {

Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials. \ LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing. \ Although this is just a first attempt towards interactive image retrieval from large egocentric datasets and there is a large room for improvement of the system components and the user interface, the structure of the system itself and the way the single components cooperate are very promising.

LEMoRe - A Lifelog Engine for Moments Retrieval at NTCIR-12 from University of Barcelona
}, url = {http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings12/NTCIR/toc_ntcir.html$\#$Lifelog}, author = {Gabriel de Oliveira-Barra and Xavier Gir{\'o}-i-Nieto and Cartas-Ayala, Alejandro and Radeva, Petia} } @conference {cGurrin, title = {LTA 2016 - The First Workshop on Lifelogging Tools and Applications}, booktitle = {ACM Multimedia}, year = {2016}, month = {10/2016}, publisher = {ACM}, organization = {ACM}, address = {Amsterdam, The Netherlands}, abstract = {

The organisation of personal data is receiving increasing research attention due to the challenges that are faced in gathering, enriching, searching and visualising this data. Given the increasing quantities of personal data being gathered by individuals, the concept of a lifelog digital library of rich multimedia and sensory content for every individual is fast becoming a reality. The LTA2016 lifelogging workshop at ACM MM 2016 aims to bring together academics and practitioners to discuss approaches to lifelog data analytics and the applications of same, and to debate the opportunities and challenges for researchers in this new and challenging area.

[Workshop web page]

[Workshop proceedings]

[UPCommons]

}, keywords = {lifelogging, Personal digital archives, Personal information management}, doi = {http://dx.doi.org/10.1145/2964284.2980534}, url = {http://lta2016.computing.dcu.ie/}, author = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Dimiccoli, M. and Johansen, H. and Joho, H. and Singh, Vivek K} } @mastersthesis {xCampos-Camunez, title = {Layer-wise CNN Surgery for Visual Sentiment Prediction}, year = {2015}, abstract = {

Advisors: Amaia Salvador (UPC), Brendan Jou (Columbia University) and Xavier Gir{\'o}-i-Nieto (UPC)

Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose. In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting. Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.

Layer-wise CNN Surgery for Visual Sentiment Prediction from Xavier Giro

}, author = {V{\'\i}ctor Campos}, editor = {Amaia Salvador and Jou, Brendan and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xBarra, title = {LIvRE: A Video Extension to the LIRE Content-Based Image Retrieval System}, year = {2015}, abstract = {

Advisors:\ Mathias Lux\ (Klagenfurt University) and\ Xavier Gir{\'o}-i-Nieto\ (UPC)

Degree: Electronic Engineering (5 years) at\ Telecom BCN-ETSETB\ (UPC)

Grade: A (9.0/10.0)

This project explores the expansion of Lucene Image Retrieval Engine (LIRE), an open-source Content-Based Image Retrieval (CBIR) system, for video retrieval on large scale video datasets. The fast growth of the need to store huge amounts of video in servers requires efficient, scalable search and indexing engines capable to assist users in their management and retrieval. In our tool, queries are formulated by visual examples allowing users to find the videos and the moment of time when the query image is matched with. The video dataset used on this scenario comprise over 1,000 hours of different news broadcast channels. This thesis presents an extension and adaptation of Lire and its plugin for Solr, an open-source enterprise search platform from the Apache Lucene project, for video retrieval based on visual features, as well as a web-interface for users from different devices.

LIvRE: A Video Extension to the LIRE Content-Based Image Retrieval System from Xavier Giro
}, url = {http://upcommons.upc.edu/handle/2117/79052}, author = {Gabriel de Oliveira-Barra}, editor = {Xavier Gir{\'o}-i-Nieto and Lux, Mathias} } @mastersthesis {xManchon-Vizuete, title = {Low computational cost algorithms for photo clustering and mail signature detection in the cloud}, year = {2014}, abstract = {

Advisors: Xavier Gir{\'o}-i-Nieto (UPC) and Omar Pera (Pixable)

Degree: Electronic Engineering (5 years) at\ Telecom BCN-ETSETB\ (UPC)

Low computational cost algorithms for photo clustering and mail signature detection in the cloud from Xavi Gir{\'o}

This Final degree thesis summarizes the tasks that have been developed during an internship in Pixable Inc. in New York City together with the tasks related to the Me- diaeval 2013 evaluation campaign, where I participated with the team of Universitat Politecnica de Catalunya (UPC). The main focus of my work was on the Photofeed service, that is a photo archive service in the cloud.

The popularisation of the storage of photos on the cloud has opened new oppor- tunities and challenges for the organization and extension of photo collections. In my thesis I have developed a light computational solution for the clustering of web photos based on social events. The proposal combines a first oversegmentation of the photo collections of each user based on temporal cues, as previously proposed in the PhotoTOC algorithm [Platt et al, PACRIM 2003]. On a second stage, the resulting mini-clusters are merged based on contextual metadata such as geolocation, keywords and user IDs.

Closely relate to photo clustering we can study mail classification too. Additional tasks were developed for the Contactive company in this field. In order to solve the problems that Contactive was facing in mail analysis tasks, I developed methods for automatically identifying signature blocks and reply lines in plain-text email messages. This analysis has many potential applications, such as preprocessing email for text-to- speech systems; anonymization of email corpora; improving automatic content-based mail classifiers; and email threading. This method is based on applying machine learning methods to a sequential representation of an email message, in which each email is represented as a sequence of lines, and each line is represented as a set of features.

Final grade: A with honors (10/10)

Daniel Manchon
}, author = {Manchon-Vizuete, Daniel}, editor = {Xavier Gir{\'o}-i-Nieto and Pera, Omar} } @conference {cSalembier14, title = {Low-level processing of PolSAR images with binary partition trees}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2014}, year = {2014}, month = {07/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Quebec, Canada}, abstract = {

This paper discusses the interest of Binary Partition Trees (BPTs) and the usefulness of graph cuts for low-level processing of PolSAR images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for many applications including filtering, segmentation, classification and object detection. Many processing strategies consist in populating the tree with a specific feature and in applying a graph-cut called pruning. Different graph-cuts are discussed and analyzed in the context of PolSAR images for speckle filtering and segmentation.

}, author = {Salembier, P. and S. Foucher and L{\'o}pez-Mart{\'\i}nez, C.} } @conference {cAlarcon11, title = {Learning to conceive, design, implement and operate circuits and systems}, booktitle = {2011 IEEE International Symposium on Circuits and Systems}, year = {2011}, pages = {1183{\textendash}1186}, isbn = {978-1-4244-9472-9}, doi = {10.1109/ISCAS.2011.5937780}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5937780\&tag=1}, author = {Alarc{\'o}n, E. and Bragos, R. and Elisa Sayrol} } @conference {cCabrera09, title = {LAVICAD: LAboratori VIrtual de Comunicacions Anal{\`o}giques i Digitals}, booktitle = {Jornada d{\textquoteright}Innovaci{\'o} Docent - RIMA (JID-RIMA)}, year = {2009}, month = {02/2009}, publisher = {UPCommons}, organization = {UPCommons}, address = {Barcelona, Catalonia}, abstract = {

Mitjan{\c c}ant el present ajut s{\textquoteright}ha ampliat l{\textquoteright}aplicaci{\'o} en xarxa LAVICAD (LAboratori VIrtual de COmunicacions Anal{\`o}giques i Digitals) que s{\textquoteright}ofereix de forma integrada dins de la plataforma d{\textquoteright}e-learning COM@WEB. LAVICAD {\'e}s una eina programada en Java i Matlab i est{\`a} formada per un conjunt de simuladors de la capa f{\'\i}sica de sistemes de comunicacions. Tots els simuladors es presenten en xarxa i es poden utilitzar pels estudiants des de qualsevol ordinador sense necessitat d{\textquoteright}instal{\textperiodcentered}laci{\'o} de cap tipus de software especial. Durant el curs 2007 2008 s{\textquoteright}han desenvolupat entre d{\textquoteright}altres dos l{\'\i}nies de treball. D{\textquoteright}una banda s{\textquoteright}ha programat l{\textquoteright}applet que emula la capa f{\'\i}sica de la televisi{\'o} digital terrestre, com a referent per a l{\textquoteright}ensenyament de sistemes de comunicacions avan{\c c}ats. D{\textquoteright}altra banda s{\textquoteright}ha treballat en la programaci{\'o} de noves funcionalitats de l{\textquoteright}eina LAVICAD, que permeten ajudar als professors en el seguiment i avaluaci{\'o} del treball continuat dels estudiants. En particular s{\textquoteright}ha programat la generaci{\'o} d{\textquoteright}una base de dades que cont{\'e} la informaci{\'o} dels usuaris que s{\textquoteright}han connectat i els resultats obtinguts a l{\textquoteright}executar un determinat simulador. Les dues l{\'\i}nies desenvolupades han de permetre durant l{\textquoteright}actual curs, consolidar l{\textquoteright}{\'u}s dels diferents simuladors per a la doc{\`e}ncia de les assignatures implicades al projecte.

}, url = {http://hdl.handle.net/2099/7235}, author = {Cabrera, M. and Xavier Gir{\'o}-i-Nieto and Rey, F. and Gasull, A. and Casas, J. and Villares, J. and Fernandez, J. and Sala {\'A}lvarez, josep and Espinosa Fricke, Pedro and Fern{\'a}ndez, Carlos Marcos and Cort{\'e}s, S. and Farr{\'e}, Miquel {\`A}ngel} } @conference {cCabrera08, title = {Lavicad: laboratorio virtual de comunicaciones anal{\'o}gicas y digitales}, booktitle = {XXIII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {2008}, month = {09/2008}, pages = {1{\textendash}4}, address = {Madrid, Spain}, abstract = {

The presented experience consists on the {\textquotedblleft}design of{\textquotedblright} and {\textquotedblleft}experimentation with{\textquotedblright} a virtual laboratory of analog and digital communications: LAVICAD. It has been result a useful tool to verify the performance of different communication systems and signal processing techniques, topics typically integrated in undergraduated courses of the curriculum of telecommunications engineering. The communication systems have been implemented and designed as Java applets and are free access. They can be run at the e-learning platform: comweb.upc.edu. The different communication systems present different levels of user interactivity and when students execute a system integrated in a comweb course, the obtained results can be supervised by the professor as an evaluation and assessment tool. From a pedagogical point of view, the main advantages of using a virtual laboratory supposes, can leads to facilitate the learning of certain matters, acting as a connection between the model of knowledge based on concepts and theories, and their practical understanding and experimentation.\ 

[URSI 2008 website][Program for ED track]

}, isbn = {978-84-612-6291-5}, author = {Cabrera, M. and Xavier Gir{\'o}-i-Nieto and Rey, F.} } @conference {cRuiz-Hidalgo07, title = {Long term selection of reference frame sub-blocks using MPEG-7 indexing metadata}, booktitle = {International Conference on Acoustics, Speech and Signal Processing, ICASSP 2007}, year = {2007}, month = {04/2007}, pages = {669{\textendash}672}, address = {Honolulu, Hawaii}, author = {Ruiz-Hidalgo, J. and Salembier, P.} } @article {jFigueras05, title = {Las Mancomunidades en Espa{\~n}a}, journal = {Bolet{\'\i} de la Asociaci{\'o}n de Ge{\'o}grafos Espa{\~n}oles}, number = {39}, year = {2005}, pages = {151{\textendash}176}, issn = {0212-9426}, author = {Figueras, P. and Haas, C. and Capdevila, C. and Ver{\'o}nica Vilaplana} } @article {xGiro-i-Nieto04, title = {La converg{\`e}ncia de la TV cap al PC}, year = {2004}, month = {03/2004}, institution = {Diari Avui}, type = {Newspaper}, address = {Barcelona, Catalonia}, author = {Xavier Gir{\'o}-i-Nieto} } @conference {cCabrera04, title = {LaViCAD: LABORATORIO VIRTUAL DE COMUNICACIONES ANAL{\'O}GICAS Y DIGITALES}, booktitle = {4rt. Congr{\'e}s Internacional de Doc{\`e}ncia Unversit{\`a}ria i Innovaci{\'o}}, year = {2004}, pages = {1{\textendash}20}, isbn = {84-8458-240-X}, author = {Cabrera, M. and Fernandez, J. and Berzosa, C. and Francisco, V. and Gasull, A.} }