1,720,972 research outputs found
A scalable saliency-based feature selection method with instance-level information
Get PDF© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article [Cancela, Brais, et al. «A Scalable Saliency-Based Feature Selection Method with Instance-Level Information». Knowledge-Based Systems, vol. 192, marzo de 2020, p. 105326] has been accepted for publication in "Knowledge-Based Systems". The Version of Record is available online at https://doi.org/10.1016/j.knosys.2019.105326.[Abstract]: Classic feature selection techniques remove irrelevant or redundant features to achieve a subset of relevant features in compact models that are easier to interpret and so improve knowledge extraction. Most such techniques operate on the whole dataset, but are unable to provide the user with useful information when only instance-level information is required; in other words, classic feature selection algorithms do not identify the most relevant information in a sample. We have developed a novel feature selection method, called saliency-based feature selection (SFS), based on deep-learning saliency techniques. Our algorithm works under any architecture that is trained by using gradient descent techniques (Neural Networks, SVMs, …), and can be used for classification or regression problems. Experimental results show our algorithm is robust, as it allows to transfer the feature ranking result between different architectures, achieving remarkable results. The versatility of our algorithm has been also demonstrated, as it can work either in big data environments as well as with small datasets.This research was financially supported in part by the Spanish Ministerio de Economía y Competitividad (research project TIN2015-65069-C2-1-R), by the Xunta de Galicia (research projects ED431C 2018/34 and Centro Singular de Investigación de Galicia, accreditation 2016–2019) and by the European Union (European Regional Development Fund) . We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. Brais Cancela acknowledges the support of the Xunta de Galicia under its postdoctoral program.Xunta de Galicia; ED431C 2018/3
Using deep learning for social analysis in egocentric images
Get PDFIn this work, we explore in detail and propose a system to cluster faces from unconstrained images. This system can be divided mainly in two big steps: i) align the faces and pass them through a deep convolutional neural network, and ii) cluster the face images by their feature representation
Using deep learning for social analysis in egocentric images
No full textIn this work, we explore in detail and propose a system to cluster faces from unconstrained images. This system can be divided mainly in two big steps: i) align the faces and pass them through a deep convolutional neural network, and ii) cluster the face images by their feature representation
Using deep learning for social analysis in egocentric images
No full textIn this work, we explore in detail and propose a system to cluster faces from unconstrained images. This system can be divided mainly in two big steps: i) align the faces and pass them through a deep convolutional neural network, and ii) cluster the face images by their feature representation
Understanding Target Trajectory Behavior: A Dynamic Scene Modeling Approach
Get PDF[Resumen] El análisis de comportamiento humano es uno de los campos más activos en la rama de visión por computador. Con el incremento de cámaras, especialmente en entornos controlados tales como aeropuertos, estaciones de tren o museos, se hace cada vez
más necesario el uso de sistemas automáticos que puedan catalogar la información
proporcionada. En el caso de entornos concurridos, es muy difícil el poder distinguir el comportamiento de personas en base a sus gestos, debido a la falta de visión de su cuerpo al completo. Por ende, el análisis de comportamiento se realiza en base a sus trayectorias, añadiendo técnicas de razonamiento de alto nivel para ulilizar dicha información en múltiples aplicaciones, tales como la video vigilancia o el análisis de tráfico. El propósito de esta investigación es el desarrollo de un sistema totalmente automático para el análisis de comportamiento de las personas. Por una parte, se presentan dos sistemas para el seguimiento de múltiples objetivos, así como un sistema novedoso para la re-identificación de personas, con la intención de detectar todo objeto de interés en la escena, devolviendo sus trayectorias como salida. Por otra parte, se presenta un sistema novedoso para el análisis de comportamiento basado en información del entorno de la escena. Está basado en la idea que que toda persona,cuando intenta llegar a un cierto lugar, tiende a seguir el mismo camino que suele utilizar la mayoría de la gente. Se presentan una serie de métricas para la detección de movimientos anómalos, haciendo que este método sea ideal para su utilización en sistemas de tiempo real.[Abstract] Human behavior analysis is one of the most active computer vision research fields. As the number of cameras are increased, especially in restricted environments, like airports, train stations or museums, the need of automatic systems that can catalog the information provided by the cameras becomes crucial. In the case of crowded scenes, it is very difficult to distinguish people behavior because of the lack of visual contact of the whole body. Thus, behavior analysis remains in the evaluation of trajectories, adding high-level knowledge approaches in order to use that information in several applications like video surveillance or traffic analysis.
The proposal of this research is the design of a fully-automatic human behavior
system from a distance. On the one hand, two different multiple-target tracking
methods and a target re-identification procedure are presented to detect every target in the scene, returning their trajectories as output. On the other hand, a novel behavior analysis system, which includes information about the environment, is provided. It is based in the idea that every person tries to reach a goal in the
scene following the same path the majority of people should use. An extremely fast
abnormal behavior metric is presented, providing our method with the capabilities
needed to be used in real-time scenarios[Resumo] A análise do comportamento humano é un dos campos máis activos na rama da
visión por computadora. Co incremento de cámaras, especialmente en entornos controlados tales coma aeroportos, estacións de tren ou museos, faise cada vez máis
necesario o uso de sistemas automáticos que poidan catalogar a información proporcionada.
No caso de entornos concurridos, é moi complicado de poder distinguir o comportamento de persoas dacordo cos seus xestos, debido á falta dunha visión
completa do corpo do suxeito. Por tanto, a análise de comportamento tende a realizarse
en base á traxectoria, engadindo técnicas de razoamento de alto nivel para utilizar dita información en diversas aplicacións, tales coma a video vixiancia ou a análise de tráfico. O propósito desta investigación é o desenrolo dun sistema totalmente automático
para a análise do comportamento das persoas. Por unha parte, preséntanse dous
sistemas para o seguimento de múltiples obxectivos, así coma un sistema novidoso
para a re-identificación de persoas, coa intención de detectar todo obxecto de interés
na escena, devolvendo as traxectorias asociadas como saída. Por outra parte,
preséntase un sistema novidoso para a análise de comportamente baseada na informaci
ón do entorno da escena. Está baseado na idea de que toda persoa, cando intenta acadar un certo luegar, tende a seguir o mesmo cami~no que xeralmente usa a maioría da xente. Preséntanse unha serie de métricas para a detección de movementos anómalos, facendo posible que este método poida ser utilizado en sistemas de tempo real
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Métricas para determinar la dificultad de sabotear una Rede Neuronal Convolucional
Get PDF[Resumen]En este trabajo fin de grado es un proyecto de Deep Learning, en concreto trata del estudio de una de las vulnerabilidades de hacer clasificación con redes neuronales convolucionales (CNN), los ataques adversos. Los ataques adversos cambian la imagen de entrada ligeramente de forma imperceptible para el ojo humano. Al pasar esta imagen modificada por el clasificador, la clasificación es errónea y con un porcentaje alto de confianza en la clase predicha. El problema está más cercano a un problema de “hacking”. Se pretende conocer la cantidad de modificaciones que hay que realizar en una imagen de entrada para que el clasificador cambie su predicción. A lo largo de la memoria se presenta un nuevo método, DENA, para mejorar la cuantificación de la robustez de las CNN. Con este trabajo se pretende dar a conocer estos ataques, reproducirlos y dar un valor numérico a estos cambios con el fin de poder medir la robustez de una red ante posibles ataques de este tipo.[Abstract]This final degree project is about Deep Learning, specifically about the study of one of the vulnerabilities of classifying with convolutional neural networks (CNN): the adversarial attacks. Adversarial attacks change the input image slightly imperceptibly to the human eye. When passing this modified image through the classifier, the classification is wrong and with a high percentage of confidence in the predicted class. The problem is closer to a “hacking” problem. It is try to know the amount of modifications that must be made in an input image for the classifier to change its prediction. Throughout the memory, a new method, DENA, is presented to improve the quantification of the robustness of CNNs. This work is intended to make these attacks known, reproduce them and give a numerical value to these changes in order to be able to measure the robustness of a network against possible attacks of this type.Traballo fin de grao. Enxeñaría Informática. Curso 2020/202
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