Gulrukh Ahanger

A digital on-demand video service supporting content-based queries

Video-on-demand represents a key demonstrative application for enabling multimedia technology in communication, database, and interface research. This application requires solving a number of diverse technical problems including the data synchronization problem for time-dependent data delivery. In this paper we describe the general requirements of video-on-demand and introduce a system supporting content-based retrieval and playback for the structure and content of digital motion pictures. In our model we capture domain-specific information for motion pictures and provide access to individual scenes of movies through queries on a temporal database. We describe our implementation of this service using existing workstation and storage technology.

A Survey of Technologies for Parsing and Indexing Digital Video1

Abstract In the future we.envision systems that will provide video information delivery services to customers on a very large scale. These systems must provide customers with mechanisms to select programs of their choice from live broadcasts. Customers should also be provided with easy means of browsing and accessing prerecorded digital data (e.g., distributed digital multimedia libraries), and downloading data from other information sources. To be viable for such large information sets, these systems must understand customer preferences and tailor the available information to the customers needs. To support this vision, a number of issues must be addressed and obstacles overcome. Intuitive interfaces, powerful query formulation and evaluation techniques, comprehensive data models, and flexible presentation functionalities must be developed. To realize these components, an effective query evaluation engine with the capabilities of query resolution in different content-specific formats (e.g., by graphics, by image, by sound) and in different domain-specific models (e.g., database of movies, database of newsclips) should be present. Additionally, the digital video database will require an efficient indexing system for easy access to the stored information. In this paper we discuss existing research trends in this area and requirements for future data delivery systems. An overview of video indexing is presented followed by a discussion on current indexing techniques.

An Annotation Engine for Supporting Video Database Population

Segmentation, video data modeling, and annotation are indispensable operations necessary for creating and populating a video database. To support such video databases, annotation data can be collected as metadata for the database and subsequently used for indexing and query evaluation. In this paper we describe the design and development of a video annotation engine, called Vane, intended to solve this problem as a domain-independent video annotation application.Using the Vane tool, the annotation of raw video data is achieved through metadata collection. This process, which is performed semi-automatically, produces tailored SGML documents whose purpose is to describe information about the video content. These documents constitute the metadatabase component of the video database. The video data model which has been developed for the metadata, is as open as possible for multiple domain-specific applications. The tool is currently in use to annotate a video archive comprised of educational and news video content.

Automatic composition techniques for video production

Video production involves the process of capturing, editing and composing video segments for delivery to a consumer. A composition must yield a coherent presentation of an event or narrative. This process can be automated if appropriate domain-specific metadata are associated with video segments and composition techniques are established. Automation leads to the support of dynamic composition and customization for applications such as news on demand. In this paper, we present techniques to achieve dynamic, real-time and cohesive video composition and customization. We also identify metrics for evaluating our techniques with respect to existing manually produced video-based news. The results of such an evaluation show that the quality of automatic composition is comparable to-and in some cases, better than-broadcast news video composition. The results also validate the assertions on which the automatic composition techniques are based.

Video query formulation

For developing advanced query formulation methods for general multimedia data, we describe the issues related to video data. We distinguish between the requirements for image retrieval and video retrieval by identifying queryable attributes unique to video data, namely audio, temporal structure, motion, and events. Our approach is based on visual query methods to describe predicates interactively while providing feedback that is as similar as possible to the video data. An initial prototype of our visual query system for video data is presented.

SELECTION AND DISSEMINATION OF DIGITAL VIDEO VIA THE VIRTUAL VIDEO BROWSER

The Virtual Video Browser (VVB) is a manifestation of our mechanisms for the location, identification, and delivery of digital audio and video in a distributed system which can be extended to several application domains including multimedia-based home entertainment, catalog shopping, and distance learning. In the following sections we describe the VVB software application designed to allow the interactive browsing and content-based query of a video database and to facilitate the subsequent playout of selected titles.

Data semantics for improving retrieval performance of digital news video systems

We propose a novel four-step hybrid approach for retrieval and composition of video newscasts based on information contained in different metadata sets. In the first step, we use conventional retrieval techniques to isolate video segments from the data universe using segment metadata. In the second step, retrieved segments are clustered into potential news items using a dynamic technique sensitive to the information contained in the segments. In the third step, we apply a transitive search technique to increase the recall of the retrieval system. In the final step, we increase recall performance by identifying segments possessing creation-time relationships. A quantitative analysis of the performance of the process on a newscast composition shows an increase in recall by 59 percent over the conventional keyword-based search technique used in the first step.