Multimedia information retrieval using artificial neural network

Abstract

The importance of the multimedia information retrieval (MIR) is highlighted by the extensive amount of the information on the internet. Image, audio, video, and text are all examples of the characteristics of the raw multimedia data. It is greatly challenging to represent a concept of human perception and how the machine-level language can grasp it (semantic gap of MIR). However, this paper aims to improve the information retrieval model that retrieves data from multimedia. This can be implemented by leveraging the use of variety of algorithms that go through training and testing to extract the model. One of these algorithms extracts text information based on the query language's nature as the vector space model (VSM) and the latent semantic index (LSI) were used. The other technique uses curvelet decomposition and statistic parameters like mean, standard deviation, and signal energy to recover these properties. Additionally, a discrete wavelet transforms (DWT) and signal characteristics-based method is used to retrieve audio signals. Finally, the neural network learning is modeled and trained on a collection of different multimedia images. The learned features have been utilized for presenting a highly sufficient system of multimedia retrieval which operates for a large set of multi-modal datasets.