Improving Statistical Multimedia Information Retrieval (MIR) Model by using Ontology and Various Information Retrieval (IR) Approaches
The process of retrieval of relevant information from massive collection of documents, either multimedia or text documents is still a cumbersome task. Multimedia documents include various elements of different data types including visible and audible data types (text, images and video documents), structural elements as well as interactive elements. In this paper, the authors have proposed a statistical high level multimedia IR model that is unaware of the shortcomings caused by classical statistical model. It involves use of ontology and different statistical IR approaches (extended Boolean approach, Bayesian network model, etc.) for representation of extracted text-image terms or phrases.