Rethinking summarization and storytelling for modern social multimedia

Traditional summarization initiatives have been focused on specific types of documents such as articles, reviews, videos, image feeds, or tweets, a practice which may result in pigeonholing the summarization task in the context of modern, content-rich multimedia collections. Consequently, much of the research to date has revolved around mostly toy problems in narrow domains and working on single-source media types. We argue that summarization and story generation systems need to re-focus the problem space in order to meet the information needs in the age of user-generated content in different formats and languages. Here we create a framework for flexible multimedia storytelling. Narratives, stories, and summaries carry a set of challenges in big data and dynamic multi-source media that give rise to new research in spatial-temporal representation, viewpoint generation, and explanatio

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

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Automatic non-linear video editing for home video collections

The video editing process consists of deciding what elements to retain, delete, or combine from various video sources so that they come together in an organized, logical, and visually pleasing manner. Before the digital era, non-linear editing involved the arduous process of physically cutting and splicing video tapes, and was restricted to the movie industry and a few video enthusiasts. Today, when digital cameras and camcorders have made large personal video collections commonplace, non-linear video editing has gained renewed importance and relevance. Almost all available video editing systems today are dependent on considerable user interaction to produce coherent edited videos. In this work, we describe an automatic non-linear video editing system for generating coherent movies from a collection of unedited personal videos. Our thesis is that computing image-level visual similarity in an appropriate manner forms a good basis for automatic non-linear video editing. To our knowledge, this is a novel approach to solving this problem. The generation of output video from the system is guided by one or more input keyframes from the user, which guide the content of the output video. The output video is generated in a manner such that it is non-repetitive and follows the dynamics of the input videos. When no input keyframes are provided, our system generates "video textures" with the content of the output chosen at random. Our system demonstrates promising results on large video collections and is a first step towards increased automation in non-linear video editin

Summarizing BBC Rushes the Informedia Way

For the first time in 2007, TRECVID considered structured evaluation of automated video summarization, utilizing BBC rushes video. This paper discusses in detail our approaches for producing the submitted summaries to TRECVID, including the two baseline methods. The cluster method performed well in terms of coverage, and adequately in terms of user satisfaction, but did take longer to review. We conducted additional evaluations using the same TRECVID assessment interface to judge 2 additional methods for summary generation: 25x (simple speed-up by 25 times), and pz (emphasizing pans and zooms). Data from 4 human assessors shows significant differences between the cluster, pz, and 25x approaches. The best coverage (text inclusion performance) is obtained by 25x, but at the expense of taking the most time to evaluate and perceived as the most redundant. Method pz was easier to use than cluster and had better performance on pan/zoom recall tasks, leading into discussions on how summaries can be improved with more knowledge of the anticipated users and tasks