A Survey of Technologies for Parsing and Indexing Digital Video1

Abstract

trieve data. Since the potential for applications like live broadcast, video-on-demand, and digital libraries is enorIn the future we envision systems that will provide video information delivery services to customers on a very large scale. mous, the challenges presented by these applications, These systems must provide customers with mechanisms to though formidable, must be met. To do so we must consider select programs of their choice from live broadcasts. Customers and understand the following issues:should also be provided with easy means of browsing and ac-• Why traditional database retrieval techniques are incessing prerecorded digital data (e.g., distributed digital multimedia libraries), and downloading data from other information sufficient for multimedia retrieval (e.g., why can we not sources. To be viable for such large information sets, these just index and retrieve images using keywords?).systems must understand customer preferences and tailor the • How spatial visual data types differ from alphanumeric available information to the customer's needs. To support this types in terms of data modeling. vision, a number of issues must be addressed and obstacles• What the roles of the user and the system are in perovercome. Intuitive interfaces, powerful query formulation and forming queries. evaluation techniques, comprehensive data models, and flexible presentation functionalities must be developed. To realize theseVisual data are perceived differently by different people. components, an effective query evaluation engine with the capa-Because of the visual nature of video data, we end up with bilities of query resolution in different content-specific formats numerous interpretations of the same data. To represent (e.g., by graphics, by image, by sound) and in different domain-all the different interpretations by keywords (text) is an specific models (e.g., database of movies, database of newsclips) impossible task as one cannot foresee all possible interpreshould be present. Additionally, the digital video database will tations of the data during indexing. Also, representation require an efficient indexing system for easy access to the stored of a small segment of video data by a large number of information. In this paper we discuss existing research trends keywords will lead to space explosion during indexing.in this area and requirements for future data delivery systems. Keywords cannot successfully represent the temporal naAn overview of video indexing is presented followed by a discusture of video data nor do they support semantic relationsion on current indexing techniques.

A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

“…Ahanger et al [3] discuss requirements and global architectures for video indexing. Some video segmentation methods are presented in this framework.…”

“…The number of shot change detection methods is now important and several reviews of these methods have been done [3,42,17,7,48,57,44,14,56,22,88,31,32,59]. These reviews often present the different methods and their efficiency based on some quality measures.…”

Video shot segmentation using fusion of SVD and mutual information features

“…The difference measure is computed either at a pixel level or at a block level. Noticing the weakness (hight sensitivity to the object and camera motions) of pixel differencing methods, many researchers suggested the use of different measures based on global information such as intensity histograms or color histograms [3], [4], [5]. The use of more complex features, such as image edges or motion vectors [6], improves the situation, but does not solve the problem completely [7].…”

A Content Based Video Retrieval Analysis System with Extensive Features by Using Kullback-Leibler

“…As video extends into both the spatial and the temporal domain, we need techniques for the sequential decomposition of footage so that exact content can be read [4] [2]. Visual information is generally perceived and construed differently by individual users because pictorial demonstration of information is frequently less specific than its textual representation [7] [8] .The growing amount of digitally produced images wants new methods to records and right to use this data. Conventional databases sanction for textual searches on Meta data only.…”