We present a method for browsing videos by directly dragging their content. This method brings the benefits of direct manipulation to an activity typically mediated by widgets. We support this new type of interactivity by: 1) automatically extracting motion data from videos; and 2) a new technique called relative flow dragging that lets users control video playback by moving objects of interest along their visual trajectory. We show that this method can outperform the traditional seeker bar in video browsing tasks that focus on visual content rather than time. ACM Classification: H5.2 [Information interfaces and presentation]: User Interfaces.- Graphical user interfaces.

Figure 1. Our SmartPlayer is adopted by the metaphor of scenic car driving. In this paper we propose a new video interaction model called adaptive fast-forwarding to help people quickly browse videos with predefined semantic rules. This model is designed around the metaphor of “scenic car driving, ” in which the driver slows down near areas of interest and speeds through unexciting areas. Results from a preliminary user study of our video player suggest the following: (1) the player should adaptively adjust the current playback speed based on the complexity of the present scene and predefined semantic events; (2) the player should learn user preferences about predefined event types as well as a suitable playback speed; (3) the player should fast-forward the video continuously with a playback rate acceptable to the user to avoid missing any undefined events or areas of interest. Furthermore, our user study results suggest that for certain types of video, our SmartPlayer yields better user experiences in browsing and fast-forwarding videos than existing video players ’ interaction models. Author Keywords Video playback, adaptive fast-forward, predefined event

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Abstract—Graphics artists commonly employ physically-based simulation for the generation of effects such as smoke, explosions, and similar phenomena. The task of finding the correct parameters for a desired result, however, is difficult and time-consuming as current tools provide little to no guidance. In this paper, we present a new approach for the visual exploration of such parameter spaces. Given a three-dimensional scene description, we utilize sampling and spatio-temporal clustering techniques to generate a concise overview of the achievable variations and their temporal evolution. Our visualization system then allows the user to explore the simulation space in a goal-oriented manner. Animation sequences with a set of desired characteristics can be composed using a novel search-by-example approach and interactive direct volume rendering is employed to provide instant visual feedback. A user study was performed to evaluate the applicability of our system in production use. Index Terms—Visual exploration, visual effects, clustering, time-dependent volume data. 1

For the first time in 2007, TRECVID considered structured evaluation of automated video summarization, utilizing BBC rushes video. In 2007, we conducted user evaluations with the published TRECVID summary assessment procedure to rate a cluster method for producing summaries, a 25x (sampling every 25th frame), 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 25x taking the most time to evaluate and judged as being the most redundant. Method pz was easier to use than cluster and rated best on redundancy. A question following the TRECVID workshop was whether simple speed-ups would still work at 50x or 100x, leading to a study with 15 human assessors looking at pzA (pz but with better audio), 25x, 50x, and 100x summaries (these latter 3 with an unsynchronized more comprehensive audio track as well). 100x gives the fastest time on task but with poor usability and performance. PzA gives the best usability measures but poor time on task and performance. 25x does well on performance as before, with 50x doing just as well but with much less time on task and better ease of use and redundancy scores. Based on these results, 50x with its audio skimming is recommended as the best way to summarize video rushes materials.

Abstract: The increasing availability of short, unstructured video clips on the Web has generated an unprecedented need to organize, index, annotate and retrieve video contents to make them useful to potential viewers. This paper presents a novel, simple, and easy-to-use tool to benchmark different low level features for video summarization based on keyframe extraction. Moreover, it shows the usefulness of the benchmarking tool by developing hypothesis for a chosen domain through an exploratory study. It discusses the results of exploratory studies involving users and their judgment of what makes the summary generated by the tool a good one. 1

Watching a long unedited video is usually a boring experience. In this paper we examine a particular subset of videos, tour videos, in which the video is captured by walking about with a running camera with the goal of conveying the essence of some place. We present a system that makes the process of sharing and watching a long tour video easier, less boring, and more informative. To achieve this, we augment the tour video with a map-based storyboard, where the tour path is reconstructed, and coherent shots at different locations are directly visualized on the map. This allows the viewer to navigate the video in the joint location-time space. To create such a storyboard we employ an automatic pre-processing component to parse the video into coherent shots, and an authoring tool to enable the user to tie the shots with landmarks on the map. The browser-based viewing tool allows users to navigate the video in a variety of creative modes with a rich set of controls, giving each viewer a unique, personal viewing experience. Informal evaluation shows that our approach works well for tour videos compared with conventional media players. ACM Classification: H5.2 [Information interfaces and presentation]:

Figure 1: Three steps in the edit-by-recommendation functionality In the world of visual storytelling, narrative development relies on a particular temporal ordering of shots and sequences and scenes. Rarely is this ordering cast in stone. Rather, the particular ordering of a story reflects a myriad of interdependent decisions about the interplay of structure, narrative arc and character development. For storytellers, particularly those developing their narratives from large documentary archives, it would be helpful to have a visualization system partnered with them to present suggestions for the most compelling story path. We present Storied Navigation, a video editing system that helps authors compose a sequence of scenes that tell a story, by selecting from a corpus of annotated clips. The clips are annotated in unrestricted natural language. Authors can also type a story in unrestricted English, and the system finds possibilities for clips that best match high-level elements of the story. Beyond simple keyword matching, these elements can include the characters, emotions, themes, and story structure. Authors can also interactively replace existing scenes or predict the next scene to continue a story, based on these characteristics. Storied Navigation gives the author the feel of brainstorming about the story rather than simply editing the media.

Abstract. We investigate the usefulness of local features in generating static video summaries. The proposed approach is based on bag of visual words using SIFT features. In an explorative experiment we compare this approach to summaries generated with the help of global features. As a resume we conclude that the local feature based approach does not outperform the other ones, however, it seems to be more stable. 1

Figure 1. The Direct Manipulation Video Player used to scrutinize a baseball pitcher’s motion: (a) pressing on the pitcher’s shoulder displays its estimated trajectory; (b) the user moves forward in the timeline by dragging the shoulder; (c) the user slowly goes back by dragging the pitcher’s wrist. The size of the pointer has been exaggerated. We present a novel method for browsing videos by directly dragging video content. This method brings the benefits of direct manipulation to an activity which is typically experienced via an indirect linear time slider. We show how direct manipulation can be supported in a video player by: 1) automatically extracting motions from video files using well-established computer vision methods; 2) using a technique called relative flow dragging that enables directly controlling the playback of these motions within the timeline of the motion rather than the overall video timeline. ACM Classification: H5.2 [Information interfaces and