An Overview of Video Shot Clustering and Summarization Techniques for Mobile Applications

The problem of content characterization of video programmes is of great interest because video appeals to large audiences and its efficient distribution over various networks should contribute to widespread usage of multimedia services. In this paper we analyze several techniques proposed in literature for content characterization of video programmes, including movies and sports, that could be helpful for mobile media consumption. In particular we focus our analysis on shot clustering methods and effective video summarization techniques since, in the current video analysis scenario, they facilitate the access to the content and help in quick understanding of the associated semantics. First we consider the shot clustering techniques based on low-level features, using visual, audio and motion information, even combined in a multi-modal fashion. Then we concentrate on summarization techniques, such as static storyboards, dynamic video skimming and the extraction of sport highlights. Discussed summarization methods can be employed in the development of tools that would be greatly useful to most mobile users: in fact these algorithms automatically shorten the original video while preserving most events by highlighting only the important content. The effectiveness of each approach has been analyzed, showing that it mainly depends on the kind of video programme it relates to, and the type of summary or highlights we are focusing on

Video Abstracting at a Semantical Level

One the most common form of a video abstract is the movie trailer. Contemporary movie trailers share a common structure across genres which allows for an automatic generation and also reflects the corresponding moviea s composition. In this thesis a system for the automatic generation of trailers is presented. In addition to action trailers, the system is able to deal with further genres such as Horror and comedy trailers, which were first manually analyzed in order to identify their basic structures. To simplify the modeling of trailers and the abstract generation itself a new video abstracting application was developed. This application is capable of performing all steps of the abstract generation automatically and allows for previews and manual optimizations. Based on this system, new abstracting models for horror and comedy trailers were created and the corresponding trailers have been automatically generated using the new abstracting models. In an evaluation the automatic trailers were compared to the original Trailers and showed a similar structure. However, the automatically generated trailers still do not exhibit the full perfection of the Hollywood originals as they lack intentional storylines across shots

A scalable approach to video summarization and adaptation

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

Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intra-session authentication, 2%-3% for inter-session authentication and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multi-modal biometric authentication system.Comment: to appear at IEEE Transactions on Information Forensics & Security; Download data from http://www.mariofrank.net/touchalytics

Gesture in Automatic Discourse Processing

Computers cannot fully understand spoken language without access to the wide range of modalities that accompany speech. This thesis addresses the particularly expressive modality of hand gesture, and focuses on building structured statistical models at the intersection of speech, vision, and meaning.My approach is distinguished in two key respects. First, gestural patterns are leveraged to discover parallel structures in the meaning of the associated speech. This differs from prior work that attempted to interpret individual gestures directly, an approach that was prone to a lack of generality across speakers. Second, I present novel, structured statistical models for multimodal language processing, which enable learning about gesture in its linguistic context, rather than in the abstract.These ideas find successful application in a variety of language processing tasks: resolving ambiguous noun phrases, segmenting speech into topics, and producing keyframe summaries of spoken language. In all three cases, the addition of gestural features -- extracted automatically from video -- yields significantly improved performance over a state-of-the-art text-only alternative. This marks the first demonstration that hand gesture improves automatic discourse processing

Recognition and Understanding of Meetings

This paper is about interpreting human communication in meetings using audio, video and other signals. Automatic meeting recognition and understanding is extremely challenging, since communication in a meeting is spontaneous and conversational, and involves multiple speakers and multiple modalities. This leads to a number of significant research problems in signal processing, in speech recognition, and in discourse interpretation, taking account of both individual and group behaviours. Addressing these problems requires an interdisciplinary effort. In this paper, I discuss the capture and annotation of multimodal meeting recordings - resulting in the AMI meeting corpus - and how we have built on this to develop techniques and applications for the recognition and interpretation of meetings

Proof of Concept For the Use of Motion Capture Technology In Athletic Pedagogy

Visualization has long been an important method for conveying complex information. Where information transfer using written and spoken means might amount to 200-250 words per minute, visual media can often convey information at many times this rate. This makes visualization a potentially important tool for education. Athletic instruction, particularly, can involve communication about complex human movement that is not easily conveyed with written or spoken descriptions. Video based instruction can be problematic since video data can contain too much information, thereby making it more difficult for a student to absorb what is cognitively necessary. The lesson is to present the learner what is needed and not more. We present a novel use of motion capture animation as an educational tool for teaching athletic movements. The advantage of motion capture is its ability to accurately represent real human motion in a minimalist context which removes extraneous information normally found in video. Motion capture animation only displays motion information, not additional information regarding the motion context. Producing an “automated coach” would be too large and difficult a problem to solve within the scope of a Master's thesis but we can perform initial steps including producing a useful software tool which performs data analysis on two motion datasets. We believe such a tool would be beneficial to a human coach as an analysis tool and the work would provide some useful understanding of next important steps towards perhaps someday producing an automated coach

Segmentation et indexation d'objets complexes dans les images de bandes dessinées

In this thesis, we review, highlight and illustrate the challenges related to comic book image analysis in order to give to the reader a good overview about the last research progress in this field and the current issues. We propose three different approaches for comic book image analysis that are composed by several processing. The first approach is called "sequential'' because the image content is described in an intuitive way, from simple to complex elements using previously extracted elements to guide further processing. Simple elements such as panel text and balloon are extracted first, followed by the balloon tail and then the comic character position in the panel. The second approach addresses independent information extraction to recover the main drawback of the first approach : error propagation. This second method is called “independent” because it is composed by several specific extractors for each elements of the image without any dependence between them. Extra processing such as balloon type classification and text recognition are also covered. The third approach introduces a knowledge-driven and scalable system of comics image understanding. This system called “expert system” is composed by an inference engine and two models, one for comics domain and another one for image processing, stored in an ontology. This expert system combines the benefits of the two first approaches and enables high level semantic description such as the reading order of panels and text, the relations between the speech balloons and their speakers and the comic character identification.Dans ce manuscrit de thèse, nous détaillons et illustrons les différents défis scientifiques liés à l'analyse automatique d'images de bandes dessinées, de manière à donner au lecteur tous les éléments concernant les dernières avancées scientifiques en la matière ainsi que les verrous scientifiques actuels. Nous proposons trois approches pour l'analyse d'image de bandes dessinées. La première approche est dite "séquentielle'' car le contenu de l'image est décrit progressivement et de manière intuitive. Dans cette approche, les extractions se succèdent, en commençant par les plus simples comme les cases, le texte et les bulles qui servent ensuite à guider l'extraction d'éléments plus complexes tels que la queue des bulles et les personnages au sein des cases. La seconde approche propose des extractions indépendantes les unes des autres de manière à éviter la propagation d'erreur due aux traitements successifs. D'autres éléments tels que la classification du type de bulle et la reconnaissance de texte y sont aussi abordés. La troisième approche introduit un système fondé sur une base de connaissance a priori du contenu des images de bandes dessinées. Ce système permet de construire une description sémantique de l'image, dirigée par les modèles de connaissances. Il combine les avantages des deux approches précédentes et permet une description sémantique de haut niveau pouvant inclure des informations telles que l'ordre de lecture, la sémantique des bulles, les relations entre les bulles et leurs locuteurs ainsi que les interactions entre les personnages