Video Storytelling: Textual Summaries for Events

Bridging vision and natural language is a longstanding goal in computer vision and multimedia research. While earlier works focus on generating a single-sentence description for visual content, recent works have studied paragraph generation. In this work, we introduce the problem of video storytelling, which aims at generating coherent and succinct stories for long videos. Video storytelling introduces new challenges, mainly due to the diversity of the story and the length and complexity of the video. We propose novel methods to address the challenges. First, we propose a context-aware framework for multimodal embedding learning, where we design a Residual Bidirectional Recurrent Neural Network to leverage contextual information from past and future. Second, we propose a Narrator model to discover the underlying storyline. The Narrator is formulated as a reinforcement learning agent which is trained by directly optimizing the textual metric of the generated story. We evaluate our method on the Video Story dataset, a new dataset that we have collected to enable the study. We compare our method with multiple state-of-the-art baselines, and show that our method achieves better performance, in terms of quantitative measures and user study.Comment: Published in IEEE Transactions on Multimedi

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

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

Smartphone picture organization: a hierarchical approach

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin

Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach

Recent years have witnessed a resurgence of interest in video summarization. However, one of the main obstacles to the research on video summarization is the user subjectivity - users have various preferences over the summaries. The subjectiveness causes at least two problems. First, no single video summarizer fits all users unless it interacts with and adapts to the individual users. Second, it is very challenging to evaluate the performance of a video summarizer. To tackle the first problem, we explore the recently proposed query-focused video summarization which introduces user preferences in the form of text queries about the video into the summarization process. We propose a memory network parameterized sequential determinantal point process in order to attend the user query onto different video frames and shots. To address the second challenge, we contend that a good evaluation metric for video summarization should focus on the semantic information that humans can perceive rather than the visual features or temporal overlaps. To this end, we collect dense per-video-shot concept annotations, compile a new dataset, and suggest an efficient evaluation method defined upon the concept annotations. We conduct extensive experiments contrasting our video summarizer to existing ones and present detailed analyses about the dataset and the new evaluation method

Activity-driven content adaptation for effective video summarisation

In this paper, we present a novel method for content adaptation and video summarization fully implemented in compressed-domain. Firstly, summarization of generic videos is modeled as the process of extracted human objects under various activities/events. Accordingly, frames are classified into five categories via fuzzy decision including shot changes (cut and gradual transitions), motion activities (camera motion and object motion) and others by using two inter-frame measurements. Secondly, human objects are detected using Haar-like features. With the detected human objects and attained frame categories, activity levels for each frame are determined to adapt with video contents. Continuous frames belonging to same category are grouped to form one activity entry as content of interest (COI) which will convert the original video into a series of activities. An overall adjustable quota is used to control the size of generated summarization for efficient streaming purpose. Upon this quota, the frames selected for summarization are determined by evenly sampling the accumulated activity levels for content adaptation. Quantitative evaluations have proved the effectiveness and efficiency of our proposed approach, which provides a more flexible and general solution for this topic as domain-specific tasks such as accurate recognition of objects can be avoided