537 research outputs found

    6 Seconds of Sound and Vision: Creativity in Micro-Videos

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    The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowdsourcing experiment to create a dataset of over 3,800 micro-videos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.Comment: 8 pages, 1 figures, conference IEEE CVPR 201

    Generación de resúmenes de videos basada en consultas utilizando aprendizaje de máquina y representaciones coordinadas

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    Video constitutes the primary substrate of information of humanity, consider the video data uploaded daily on platforms as YouTube: 300 hours of video per minute, video analysis is currently one of the most active areas in computer science and industry, which includes fields such as video classification, video retrieval and video summarization (VSUMM). VSUMM is a hot research field due to its importance in allowing human users to simplify the information processing required to see and analyze sets of videos, for example, reducing the number of hours of recorded videos to be analyzed by a security personnel. On the other hand, many video analysis tasks and systems requires to reduce the computational load using segmentation schemes, compression algorithms, and video summarization techniques. Many approaches have been studied to solve VSUMM. However, it is not a single solution problem due to its subjective and interpretative nature, in the sense that important parts to be preserved from the input video requires a subjective estimation of an importance sco- re. This score can be related to how interesting are some video segments, how close they represent the complete video, and how segments are related to the task a human user is performing in a given situation. For example, a movie trailer is, in part, a VSUMM task but related to preserving promising and interesting parts from the movie but not to be able to reconstruct the movie content from them, i.e., movie trailers contains interesting scenes but not representative ones. On the contrary, in a surveillance situation, a summary from the closed-circuit cameras needs to be representative and interesting, and in some situations related with some objects of interest, for example, if it is needed to find a person or a car. As written natural language is the main human-machine communication interface, recently some works have made advances in allowing to include textual queries in the VSUMM process which allows to guide the summarization process, in the sense that video segments related with the query are considered important. In this thesis, we present a computational framework to perform video summarization over an input video, which allows the user to input free-form sentences and keywords queries to guide the process by considering user intention or task intention, but also considering general objectives such as representativeness and interestingness. Our framework relies on the use of pre-trained deep visual and linguistic models, although we trained our visual-linguistic coordination model. We expect this model will be of interest in cases where VSUMM tasks requires a high degree of specification of user/task intentions with minimal training stages and rapid deployment.El video constituye el sustrato primario de información de la humanidad, por ejemplo, considere los datos de video subidos diariamente en plataformas cómo YouTube: 300 horas de video por minuto. El análisis de video es actualmente una de las áreas más activas en la informática y la industria, que incluye campos como la clasificación, recuperación y generación de resúmenes de video (VSUMM). VSUMM es un campo de investigación de alto dinamismo debido a su importancia al permitir que los usuarios humanos simplifiquen el procesamiento de la información requerido para ver y analizar conjuntos de videos, por ejemplo, reduciendo la cantidad de horas de videos grabados para ser analizados por un personal de seguridad. Por otro lado, muchas tareas y sistemas de análisis de video requieren reducir la carga computacional utilizando esquemas de segmentación, algoritmos de compresión y técnicas de VSUMM. Se han estudiado muchos enfoques para abordar VSUMM. Sin embargo, no es un problema de solución única debido a su naturaleza subjetiva e interpretativa, en el sentido de que las partes importantes que se deben preservar del video de entrada, requieren una estimación de una puntuación de importancia. Esta puntuación puede estar relacionada con lo interesantes que son algunos segmentos de video, lo cerca que representan el video completo y con cómo los segmentos están relacionados con la tarea que un usuario humano está realizando en una situación determinada. Por ejemplo, un avance de película es, en parte, una tarea de VSUMM, pero esta ́ relacionada con la preservación de partes prometedoras e interesantes de la película, pero no con la posibilidad de reconstruir el contenido de la película a partir de ellas, es decir, los avances de películas contienen escenas interesantes pero no representativas. Por el contrario, en una situación de vigilancia, un resumen de las cámaras de circuito cerrado debe ser representativo e interesante, y en algunas situaciones relacionado con algunos objetos de interés, por ejemplo, si se necesita para encontrar una persona o un automóvil. Dado que el lenguaje natural escrito es la principal interfaz de comunicación hombre-máquina, recientemente algunos trabajos han avanzado en permitir incluir consultas textuales en el proceso VSUMM lo que permite orientar el proceso de resumen, en el sentido de que los segmentos de video relacionados con la consulta se consideran importantes. En esta tesis, presentamos un marco computacional para realizar un resumen de video sobre un video de entrada, que permite al usuario ingresar oraciones de forma libre y consultas de palabras clave para guiar el proceso considerando la intención del mismo o la intención de la tarea, pero también considerando objetivos generales como representatividad e interés. Nuestro marco se basa en el uso de modelos visuales y linguísticos profundos pre-entrenados, aunque también entrenamos un modelo propio de coordinación visual-linguística. Esperamos que este marco computacional sea de interés en los casos en que las tareas de VSUMM requieran un alto grado de especificación de las intenciones del usuario o tarea, con pocas etapas de entrenamiento y despliegue rápido.MincienciasDoctorad

    Beautiful and damned. Combined effect of content quality and social ties on user engagement

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    User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on Knowledge and Data Engineering (Volume: PP, Issue: 99

    Semantic interpretation of events in lifelogging

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    The topic of this thesis is lifelogging, the automatic, passive recording of a person’s daily activities and in particular, on performing a semantic analysis and enrichment of lifelogged data. Our work centers on visual lifelogged data, such as taken from wearable cameras. Such wearable cameras generate an archive of a person’s day taken from a first-person viewpoint but one of the problems with this is the sheer volume of information that can be generated. In order to make this potentially very large volume of information more manageable, our analysis of this data is based on segmenting each day’s lifelog data into discrete and non-overlapping events corresponding to activities in the wearer’s day. To manage lifelog data at an event level, we define a set of concepts using an ontology which is appropriate to the wearer, applying automatic detection of concepts to these events and then semantically enriching each of the detected lifelog events making them an index into the events. Once this enrichment is complete we can use the lifelog to support semantic search for everyday media management, as a memory aid, or as part of medical analysis on the activities of daily living (ADL), and so on. In the thesis, we address the problem of how to select the concepts to be used for indexing events and we propose a semantic, density- based algorithm to cope with concept selection issues for lifelogging. We then apply activity detection to classify everyday activities by employing the selected concepts as high-level semantic features. Finally, the activity is modeled by multi-context representations and enriched by Semantic Web technologies. The thesis includes an experimental evaluation using real data from users and shows the performance of our algorithms in capturing the semantics of everyday concepts and their efficacy in activity recognition and semantic enrichment

    Annotating, Understanding, and Predicting Long-term Video Memorability

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    International audienceMemorability can be regarded as a useful metric of video importance to help make a choice between competing videos. Research on computational understanding of video memorability is however in its early stages. There is no available dataset for modelling purposes, and the few previous attempts provided protocols to collect video memorability data that would be difficult to generalize. Furthermore, the computational features needed to build a robust memorability predictor remain largely undiscovered. In this article, we propose a new protocol to collect long-term video memorability annotations. We measure the memory performances of 104 participants from weeks to years after memorization to build a dataset of 660 videos for video memorability prediction. This dataset is made available for the research community. We then analyze the collected data in order to better understand video memorability, in particular the effects of response time, duration of memory retention and repetition of visualization on video memorability. We finally investigate the use of various types of audio and visual features and build a computational model for video memorability prediction. We conclude that high level visual semantics help better predict the memorability of videos
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