Application of Semistructured Data Model to the Implementation of Semantic Content-Based Video Retrieval System

Semantic indexing of a video document is a process that performs the identification of elementary and complex semantic units in the indexed document in order to create a semantic index defined as a mapping of semantic units into the sequences of video frames. Semantic content-based video retrieval system is a software system that uses a semantic index built over a collection of video documents to retrieve the sequences of video frames that satisfy the given conditions. This work introduces a new multilevel view of data for the semantic content-based video retrieval systems. At the topmost level, we define an abstract view of data and we express it in a notation of enhanced conceptual modeling suitable for the formal representation of the semantic contents of video documents. A semistructured data model is proposed for the middle level representation of data. At the bottom level we implement a semistructured data model as an object-relational database. The completeness of the proposed approach is demonstrated through the mappings of a conceptual level into a semistructured level and into an object-relational organization of data. The paper describes a system of operations on semistructured data and shows how a sample query can be represented as an expression built from the operations.