Using Artificial Neural Network for Multimedia Information Retrieval

Abstract

Multimedia Information Retrieval (MIR) is an important field due to the great amount of information going through the Internet. Multimedia data can be considered as raw data or the features that compose it. Raw multimedia data consists of data structures with diverse characteristics such as image, audio, video, and text. The big challenge of MIR is a semantic gap, which is the difference between the human perception of a concept and how it can be represented using a machine-level language. The aim of this paper is to use different algorithms through two stages one for training and the other for testing. The first algorithm depends on the nature of the query language to retrieve the text document using two models, Vector Space Model (VSM) and Latent Semantic Index (LSI). The second algorithm is based on the extracted features using curvelet decomposition and the statistic parameters such as mean, standard deviation and energy of signals. The other algorithm is based on the discrete wavelet transform (DWT) and features of signals to retrieve audio signals, then the neural network is applied to describe the information retrieval model which retrieves the information from the multimedia. The neural network model, based on multiplayer perceptron and spreading activation network type, accepts the structure of conceptually and linguistically oriented model.