A Study on Content-Based Retrieval for Video Databases
|關鍵字:||視訊資料庫;內容擷取;視訊索引;MPEG7;相似查詢;video database;content-based retrieval;video indexing;MPEG7;similarity retrieval|
A video database system has the property that it must not only manage a collection of video data, but also provide content-based access to users. One of the most important topics in video database systems is to support content-based retrieval, on which numerous researches have been done in recent years. However, some critical issues are left for further researches. First, the systems proposed in previous work are basically suitable only for some applications. A system, which is adaptive to various system environments, is required. Second, high-level semantic concepts should be integrated into low-level audio-visual features to associate what users desire with what systems can deliver. Third, video features should be manipulated and queried efficiently. Fourth, in order to obviate the need to decompress the video data, it is efficient to search and index video data in the compressed form. Fifth, a formal computational model of simulating user-defined preference relation among video data is required to judge retrieval performance of the systems. In this dissertation, a semi-automatic content-based video retrieval system, which meets the requirements mentioned above, is proposed. The system provides a two-layered conceptual model with two major components, semantic inference model and visual aggregation model, for describing semantic and visual contents of video data, respectively. Based on available automatic scene segmentation and object tracking algorithms, the proposed model supports eight operations to manipulate the metadata at various levels of semantic abstraction and object granularity. An annotation language is designed to describe scenarios in video data and can be efficiently analyzed. With the assistance of domain knowledge and index organizations, this investigation also develops algorithms to efficiently process five types of familiar video queries: semantic query, temporal query, similar query, fuzzy query, and hybrid query. In addition, query-by-example and an SQL-like query language for video retrieval are provided. We also propose two novel methods, filtering and prediction, to improve the performance of video retrieval by example images in the compressed domain. In contrast to conventional off-line image and video indexing techniques, these methods are focused on efficient on-line video retrieval that can be applicable to the applications with time constraint such as live broadcast, surveillance and videoconference. Three algorithms are developed according to the proposed methods including scene change detection, full image query and partial image query for MPEG streams. Without decompression of video archive, the filtering and prediction approaches aim to prevent superfluous computations from filtering dissimilar images and predicting similar images, respectively. The filtering approach employs DCT features while the prediction approach macroblock and motion vector information. Through a series of simulation experiments, the system is shown to perform better than previously proposed models and matching algorithms in maximizing recall and precision. And filtering and precision methods can significantly improve computational performance.
|Appears in Collections:||Thesis|