Hierarchical modelling and adaptive clustering for real-time summarization of rush videos

In this paper, we provide detailed descriptions of a proposed new algorithm for video summarization, which are also included in our submission to TRECVID'08 on BBC rush summarization. Firstly, rush videos are hierarchically modeled using the formal language technique. Secondly, shot detections are applied to introduce a new concept of V-unit for structuring videos in line with the hierarchical model, and thus junk frames within the model are effectively removed. Thirdly, adaptive clustering is employed to group shots into clusters to determine retakes for redundancy removal. Finally, each most representative shot selected from every cluster is ranked according to its length and sum of activity level for summarization. Competitive results have been achieved to prove the effectiveness and efficiency of our techniques, which are fully implemented in the compressed domain. Our work does not require high-level semantics such as object detection and speech/audio analysis which provides a more flexible and general solution for this topic

On-line video abstraction

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, abril de 201

A scalable approach to video summarization and adaptation

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 201

Multi-modal surrogates for retrieving and making sense of videos: is synchronization between the multiple modalities optimal?

Video surrogates can help people quickly make sense of the content of a video before downloading or seeking more detailed information. Visual and audio features of a video are primary information carriers and might become important components of video retrieval and video sense-making. In the past decades, most research and development efforts on video surrogates have focused on visual features of the video, and comparatively little work has been done on audio surrogates and examining their pros and cons in aiding users' retrieval and sense-making of digital videos. Even less work has been done on multi-modal surrogates, where more than one modality are employed for consuming the surrogates, for example, the audio and visual modalities. This research examined the effectiveness of a number of multi-modal surrogates, and investigated whether synchronization between the audio and visual channels is optimal. A user study was conducted to evaluate six different surrogates on a set of six recognition and inference tasks to answer two main research questions: (1) How do automatically-generated multi-modal surrogates compare to manually-generated ones in video retrieval and video sense-making? and (2) Does synchronization between multiple surrogate channels enhance or inhibit video retrieval and video sense-making? Forty-eight participants participated in the study, in which the surrogates were measured on the the time participants spent on experiencing the surrogates, the time participants spent on doing the tasks, participants' performance accuracy on the tasks, participants' confidence in their task responses, and participants' subjective ratings on the surrogates. On average, the uncoordinated surrogates were more helpful than the coordinated ones, but the manually-generated surrogates were only more helpful than the automatically-generated ones in terms of task completion time. Participants' subjective ratings were more favorable for the coordinated surrogate C2 (Magic A + V) and the uncoordinated surrogate U1 (Magic A + Storyboard V) with respect to usefulness, usability, enjoyment, and engagement. The post-session questionnaire comments demonstrated participants' preference for the coordinated surrogates, but the comments also revealed the value of having uncoordinated sensory channels

REAL-TIME EVENT SUMMARIZATION OF ESPORTS EVENT THROUGH TWITTER STREAMS

Exploration of real-time summarization (RTS) methodologies and applications to esports events on Twitter. The goal of this study is to evaluate the effectiveness of real-time summarization techniques at esports event detection, highlight summarization, and timeline generation. A two-step system of event-prediction and summarization is proposed. First, using Twitter as the data source, events in an esports game are predicted through machine-learning and classification to determine event occurrences. Four major classification features and five standard classification models (Naive Bayes, Logistic Regression, Decision Trees, K-Nearest Neighbors, Support Vector Machines) are evaluated to identify an optimal event-detection model. Second, natural-language text processing functions such as term-frequency and TF.IDF are evaluated for effective event summarization and to confirm successful event-detection. The CART (classification and regression tree) model is selected as the most optimal model for event-detection, predicting in-game esports events with 70% accuracy. This study demonstrates the application of Twitter as a data source in detecting real-time esports events.Master of Science in Information Scienc

Automatic movie analysis and summarisation

Automatic movie analysis is the task of employing Machine Learning methods to the field of screenplays, movie scripts, and motion pictures to facilitate or enable various tasks throughout the entirety of a movie’s life-cycle. From helping with making informed decisions about a new movie script with respect to aspects such as its originality, similarity to other movies, or even commercial viability, all the way to offering consumers new and interesting ways of viewing the final movie, many stages in the life-cycle of a movie stand to benefit from Machine Learning techniques that promise to reduce human effort, time, or both. Within this field of automatic movie analysis, this thesis addresses the task of summarising the content of screenplays, enabling users at any stage to gain a broad understanding of a movie from greatly reduced data. The contributions of this thesis are four-fold: (i)We introduce ScriptBase, a new large-scale data set of original movie scripts, annotated with additional meta-information such as genre and plot tags, cast information, and log- and tag-lines. To our knowledge, Script- Base is the largest data set of its kind, containing scripts and information for almost 1,000 Hollywood movies. (ii) We present a dynamic summarisation model for the screenplay domain, which allows for extraction of highly informative and important scenes from movie scripts. The extracted summaries allow for the content of the original script to stay largely intact and provide the user with its important parts, while greatly reducing the script-reading time. (iii) We extend our summarisation model to capture additional modalities beyond the screenplay text. The model is rendered multi-modal by introducing visual information obtained from the actual movie and by extracting scenes from the movie, allowing users to generate visual summaries of motion pictures. (iv) We devise a novel end-to-end neural network model for generating natural language screenplay overviews. This model enables the user to generate short descriptive and informative texts that capture certain aspects of a movie script, such as its genres, approximate content, or style, allowing them to gain a fast, high-level understanding of the screenplay. Multiple automatic and human evaluations were carried out to assess the performance of our models, demonstrating that they are well-suited for the tasks set out in this thesis, outperforming strong baselines. Furthermore, the ScriptBase data set has started to gain traction, and is currently used by a number of other researchers in the field to tackle various tasks relating to screenplays and their analysis

Intelligent Software Tooling For Improving Software Development

Software has eaten the world with many of the necessities and quality of life services people use requiring software. Therefore, tools that improve the software development experience can have a significant impact on the world such as generating code and test cases, detecting bugs, question and answering, etc. The success of Deep Learning (DL) over the past decade has shown huge advancements in automation across many domains, including Software Development processes. One of the main reasons behind this success is the availability of large datasets such as open-source code available through GitHub or image datasets of mobile Graphical User Interfaces (GUIs) with RICO and ReDRAW to be trained on. Therefore, the central research question my dissertation explores is: In what ways can the software development process be improved through leveraging DL techniques on the vast amounts of unstructured software engineering artifacts? We coin the approaches that leverage DL to automate or augment various software development task as Intelligent Software Tools. To guide our research of these intelligent software tools, we performed a systematic literature review to understand the current landscape of research on applying DL techniques to software tasks and any gaps that exist. From this literature review, we found code generation to be one of the most studied tasks with other tasks and artifacts such as impact analysis or tasks involving images and videos to be understudied. Therefore, we set out to explore the application of DL to these understudied tasks and artifacts as well as the limitations of DL models under the well studied task code completion, a subfield in code generation. Specifically, we developed a tool for automatically detecting duplicate mobile bug reports from user submitted videos. We used the popular Convolutional Neural Network (CNN) to learn important features from a large collection of mobile screenshots. Using this model, we could then compute similarity between a newly submitted bug report and existing ones to produce a ranked list of duplicate candidates that can be reviewed by a developer. Next, we explored impact analysis, a critical software maintenance task that identifies potential adverse effects of a given code change on the larger software system. To this end, we created Athena, a novel approach to impact analysis that integrates knowledge of a software system through its call-graph along with high-level representations of the code inside the system to improve impact analysis performance. Lastly, we explored the task of code completion, which has seen heavy interest from industry and academia. Specifically, we explored various methods that modify the positional encoding scheme of the Transformer architecture for allowing these models to incorporate longer sequences of tokens when predicting completions than seen during their training as this can significantly improve training times

Improving Collection Understanding for Web Archives with Storytelling: Shining Light Into Dark and Stormy Archives

Collections are the tools that people use to make sense of an ever-increasing number of archived web pages. As collections themselves grow, we need tools to make sense of them. Tools that work on the general web, like search engines, are not a good fit for these collections because search engines do not currently represent multiple document versions well. Web archive collections are vast, some containing hundreds of thousands of documents. Thousands of collections exist, many of which cover the same topic. Few collections include standardized metadata. Too many documents from too many collections with insufficient metadata makes collection understanding an expensive proposition. This dissertation establishes a five-process model to assist with web archive collection understanding. This model aims to produce a social media story – a visualization with which most web users are familiar. Each social media story contains surrogates which are summaries of individual documents. These surrogates, when presented together, summarize the topic of the story. After applying our storytelling model, they summarize the topic of a web archive collection. We develop and test a framework to select the best exemplars that represent a collection. We establish that algorithms produced from these primitives select exemplars that are otherwise undiscoverable using conventional search engine methods. We generate story metadata to improve the information scent of a story so users can understand it better. After an analysis showing that existing platforms perform poorly for web archives and a user study establishing the best surrogate type, we generate document metadata for the exemplars with machine learning. We then visualize the story and document metadata together and distribute it to satisfy the information needs of multiple personas who benefit from our model. Our tools serve as a reference implementation of our Dark and Stormy Archives storytelling model. Hypercane selects exemplars and generates story metadata. MementoEmbed generates document metadata. Raintale visualizes and distributes the story based on the story metadata and the document metadata of these exemplars. By providing understanding immediately, our stories save users the time and effort of reading thousands of documents and, most importantly, help them understand web archive collections