Efficient Video Indexing on the Web: A System that Leverages User Interactions with a Video Player

In this paper, we propose a user-based video indexing method, that automatically generates thumbnails of the most important scenes of an online video stream, by analyzing users' interactions with a web video player. As a test bench to verify our idea we have extended the YouTube video player into the VideoSkip system. In addition, VideoSkip uses a web-database (Google Application Engine) to keep a record of some important parameters, such as the timing of basic user actions (play, pause, skip). Moreover, we implemented an algorithm that selects representative thumbnails. Finally, we populated the system with data from an experiment with nine users. We found that the VideoSkip system indexes video content by leveraging implicit users interactions, such as pause and thirty seconds skip. Our early findings point toward improvements of the web video player and its thumbnail generation technique. The VideSkip system could compliment content-based algorithms, in order to achieve efficient video-indexing in difficult videos, such as lectures or sports.Comment: 9 pages, 3 figures, UCMedia 2010: 2nd International ICST Conference on User Centric Medi

A Fast Image Segmentation Algorithm for Interactive Video Hotspot Retrieval

This paper presents a fast image segmentation algorithm for extracting areas-of-interest (”hotspots”) from video sequences. Because video hotspots, which are the visual analogues of HTML hyperlinks, highly depend on a user’s interest, it is very difficult to automatically extract the exact outlines of objects of interest in videos. Our approach of interactive video hotspot retrieval is to provide candidate hotspots with segmented regions of video frames when requested, and web designers using the tool for web site creation may select the area (or areas) he is interested in as a video hotspot. This segmentation algorithm follows a three step coarse-to-fine scheme. The original image is first divided into blocks and a block-merging process is applied to get a coarse segmentation. Then we represent the presegmented image as a graph where the value of edges in this graph are evaluated based on multi-features, and by using a minimum spanning tree searching algorithm, we merge the vertices in the graph to generate the segmented regions of the image. Finally, we refine the boundary of segmented regions on the pixel level using an improved K-means clustering algorithm. This segmentation algorithm is fast and efficient and our final segmentation results compare favorably with the results obtained using manual outlining. We also provide a convenient way for the web page designer to select multiple regions as a video hotspot. Our method allows the designer to click on an image during video playback and to get a segmented image almost instantly. The extracted video hotspots can then be used for video hyperlinks, an extension of the general concept of hyperlink to interactive videos

Multimedia Authoring Systems

In this paper we have surveyed three multimedia authoring systems (Multimedia Toolbook 3.0, Director 4.0, and Authorware 3.0). Each system uses a different metaphor (book, movie, and icon-based flowchart) for creating multimedia applications. A sample application is developed in each MAS and the effects of the corresponding metaphors are compared. Technologies like ODBC, OLE, DDE, DLL, and MCI are discussed and we have shown how MAS's can benefit from them. We have also discussed the limitations of the current systems, and where the research in this area is headed. 2 Introduction Multimedia software applications have become a multi-billion dollar business. Typical multimedia applications include: ffl Product demos (e.g. Multimedia Toolkit demo) ffl Kiosk applications (e.g. concert ticket ordering) ffl Computer Based Training (CBT) (e.g. Microsoft Excel tutorial) ffl Games (e.g. Iron Helix is written in Director) ffl Multimedia References (e.g. MS Encarta, 747 repair manual) ffl ..

Efficient and Cost-effective Techniques for Browsing and Indexing Large Video Databases

We present in this paper a fully automatic content-based approach to organizing and indexing video data. Our methodology involves three steps: ffl Step 1: We segment each video into shots using a Camera-Tracking technique. This process also extracts the feature vector for each shot, which consists of two statistical variances V ar BA and V ar OA . These values capture how much things are changing in the background and foreground areas of the video shot. ffl Step 2: For each video, We apply a fully automatic method to build a browsing hierarchy using the shots identified in Step 1. ffl Step 3: Using the V ar BA and V ar OA values obtained in Step 1, we build an index table to support a variance-based video similarity model. That is, video scenes/shots are retrieved based on given values of V ar BA and V ar OA . The above three inter-related techniques offer an integrated framework for modeling, browsing, and searching large video databases. Our experimental results indic..

From Theory to Practice: Teaching Assistants’ Role in Multi-Campus Education

Teaching Assistants (TAs) are an indispensable part of higher education. TAs have two identities simultaneously: those of a student and of a teacher. However, the role of TAs’ may not be clearly defined, resulting in a role ambiguity. In this study, we have researched the TAs’ role in multi-campus education. Data for this research was collected through interviews with eight TAs at a Scandinavian multi-campus university who took a multi-campus TA role for the first time. This paper summarizes these TAs’ work tasks in multi-campus courses. Their tasks included setting up technology, dealing with technical problems, and communicating with other TAs in the course. Our study suggests that TAs’ challenges may be reduced if technical staff is caring for ICT equipment and technical problems, if direct interaction is provided between teacher and students, and if local teaching staff is involved in the activities at the remote campus