On the automated interpretation and indexing of American football

This work combines natural language understanding and image processing with incremental learning to develop a system that can automatically interpret and index American Football. We have developed a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain. Our representation combines expert knowledge, domain knowledge, spatial knowledge and temporal knowledge. We also present an incremental learning algorithm to improve the knowledge base as well as to keep previously developed concepts consistent with new data. The advantages of the incremental learning algorithm are that is that it does not split concepts and it generates a compact conceptual hierarchy which does not store instances.

Networking of Multimedia Women Event : beyond epsilon science - "where to look and how to realize new opportunities"

"Networking of Multimedia Women" event is a continuation of an on-going conversation in the multimedia research community and efforts by the ACM SIGMM to engage and promote female researchers in multimedia community, enable networking of junior and senior female researchers, and give insights towards successful professional careers based on examples. This year, the event will have a theme, called "Beyond Epsilon Science", where preeminent senior female researchers from academia, industry and government, Svetha Venkatesh, Nalini Venkatasubramanian, Dulce Ponceleon, Susanne Boll, and Maria Zemankova will present and discuss how to go beyond epsilon science, where to look for big ideas with high social impact, as well as how to obtain funding to realize these ideas, innovations and opportunities. Their current research projects and funding efforts, and their personal experiences will drive the event's discussions, awareness of major research and funding initiatives, answers to open questions and insights into successful professional careers.

Cognitive intervention in autism using multimedia stimulus

We demonstrate an open multimedia-based system for delivering early intervention therapy for autism. Using exible multi-touch interfaces together with principled ways to access rich content and tasks, we show how a syllabus can be translated into stimulus sets for early intervention. Media stimuli are able to be presented agnostic to language and media modality due to a semantic network of concepts and relations that are fundamental to language and cognitive development, which enable stimulus complexity to be adjusted to child performance. Being open, the system is able to assemble enough media stimuli to avoid children over-learning, and is able to be customised to a specific child which aids with engagement. Computer-based delivery enables automation of session logging and reporting, a fundamental and time-consuming part of therapy.

Contextual navigation in a multimedia journal

This work presents a framework for multimedia journaling,maintaining strong relationships between the document and embedded media. This enables media archives that are robust to changes in software environments, such as changes in web-sharing services, proprietary file formats and enables portability across operating system. We develop a journaling application using an existing multimedia framework, and show the power of the paradigm with specific case studies.

Between context-aware media capture and multimedia content analysis - where do we find the promised land?

Various issues related to the multimedia information retrieval and media access are discussed. The feasible solutions for automatic signal-based analysis of media content are analyzed. The extent of user involvement in the content creation process is emphasized. The applications driving the creation and usage of context and metadata are also elaborated.

Application of computational media aesthetics methodology to extracting color semantics in film

Using film grammar as the underpinning, we study the extraction of structures in video based on color using a wide configuration of clustering methods combined with existing and new similarity measures. We study the visualisation of these structures, which we call Scene-Cluster Temporal Charts and show how it can bring out the interweaving of different themes and settings in a film. We also extract color events that filmmakers use to draw/force a viewer's attention to a shot/scene. This is done by first extracting a set of colors used rarely in film, and then building a probabilistic model for color event detection. We demonstrate with experimental results from ten movies that our algorithms are effective in the extraction of both scene-cluster temporal charts and color events.

Indexing narrative structure and semantics in motion pictures with a probabilistic framework

This work constitutes the first attempt to extract an important narrative structure, the 3-Act story telling paradigm, in film. This narrative structure is prevalent in the domain of film as it forms the foundation and framework in which the film can be made to function as an effective tool for story telling, and its extraction is a vital step in automatic content management for film data. A novel act boundary likelihood function for Act 1 is derived using a Bayesian formulation under guidance from film grammar, tested under many configurations and the results are reported for experiments involving 25 full length movies. The formulation is shown to be a useful tool in both the automatic and semi-interactive setting for semantic analysis of film.

On the automatic indexing of cricket using camera motion parameters

This paper describes an application of camera motion estimation to index cricket games. The shots are labeled with the type of shot: glance left, glance right, left drive, right drive, left cut, right pull and straight drive. The method has the advantages that it is fast and avoids complex image segmentation. The classification of the cricket shots is done using an incremental learning algorithm. We tested the method on over 600 shots and the results show that the system has a classification accuracy of 74%.

Video indexing by spatial representation

Recent approaches to video indexing and retrieval are either pixel-oriented or object-oriented. While the former approaches focus on motion and changes thereto, the latter focus on spatial relations among objects in the scene. In this paper, a spatial knowledge representation technique combining both approaches is proposed. This representation supplements the spatial knowledge of visual objects with information about their pixel positions in the video frame. It provides a practical way to construct video indices, enabling searching for and retrieval of video sequences that contain motion as well as sparsely disjoint objects

An efficient least common subgraph algorithm for video indexing

Many tasks in computer vision can be expressed as graph problems. This allows the task to be solved using a well studied algorithm, however many of these algorithms are of exponential complexity. This is a disadvantage when considered in the context of searching a database of images or videos for similarity. Work by Mesaner and Bunke (1995) has suggested a new class of graph matching algorithms which uses a priori knowledge about a database of models to reduce the time taken during online classification. This paper presents a new algorithm which extends the earlier work to detection of the largest common subgraph.