Preface

Due to increasing globalization and the explosion of media available on the Internet, computer techniques to organize, classify, and find desired media are becoming more and more relevant. To extract semantic information from multimedia data sources, the Multimedia Information Retrieval (MMIR or MIR) technique has been applied. This evolution continues to grow, as multimedia sensors are present in diverse contexts.

MIR is a broad area covering both structural issues and intelligent content analysis and retrieval. These aspects must be integrated into a seamless whole, which involves expertise from a wide variety of fields. This book presents recent applications of MIR in content-based image retrieval and fusion, bioinformation analysis and processing, forensic multimedia retrieval techniques, and audio and music classification.

The book is organized into four sections. The first two sections include two chapters each, and the last two sections contain one chapter each. Section 1, "Content-Based Image Retrieval and Fusion," discusses the application of computer techniques to search for videos and images in large databases, as well as mechanisms to apply multiple-image fusion. Section 2, "Bioinformation Analysis and Processing," examines information extraction and processing in biomedical applications. Section 3, "Forensic Multimedia Retrieval Techniques," provides the design and test of a framework addressing the challenges of evidence collection in critical infrastructures are provided. Finally, Section 4, "Audio and Music Classification and Separation," provides a full review of classification and separation algorithms for audio and music signals.

Section 1: "Content-Based Image Retrieval and Fusion"

Chapter 1, "Towards Large-Scale Image Retrieval System Using Parallel Frameworks", highlights that the increasing use of mobile devices such as smartphones has resulted in a dramatic increase in the number of images collected every day. Consequently, this chapter introduces some fundamental theories for content-based image retrieval for large-scale databases using parallel frameworks. The main issues and basic concepts of Big Data and its technologies are discussed, moving towards modern tools including cutting-edge storage platforms.

Chapter 2 "Multiple-Image Fusion Encryption (MIFE) Using Discrete Cosine Transformation (DCT) and Pseudo Random Number Generators", proposes a new multiple-image encryption algorithm based on the spectral fusion of watermarked images and new chaotic generators. First, the discrete cosine transformation and the low-pass filter of appropriate sizes are used to combine the target watermarked images in the spectral domain in two different multiplex images. Second, each of the two images is concatenated into blocks of small size, which are mixed by changing their position following the order generated by a chaotic sequence from the Logistic-May system. Finally, the fusion of both scrambled images is achieved by a nonlinear mathematical expression based on Cramer's rule to obtain two hybrid encrypted images. Then, after the decryption step, the hidden message can be retrieved from the watermarked image without any loss.

Section 2: "Bioinformation Analysis and Processing"

Chapter 3, "Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications", includes an overview of information extraction techniques for hyperspectral imaging in biomedical applications. First, it presents the background of hyperspectral imaging and the main motivations of its usage for medical applications. Second, it discusses information extraction techniques based on both light propagation models within tissue and machinelearning approaches. Third, it examines the usage of such information extraction techniques in hyperspectral imaging biomedical research applications. Finally, it discusses the main advantages and disadvantages of the most commonly used image processing approaches, along with the current challenges in HSI information extraction techniques in clinical applications.

Chapter 4, "A Hybrid Image Fusion Algorithm for Medical Applications", proposes a hybrid fusion approach for brain medical imaging based on two stages. The initial stage deals with the enhancement of a computed tomography scan image exploitation, a novel with respect to other techniques such as bar graph equalization or adaptation bar graph. In the second stage, the improved computed tomography scan image is joined to tomography image exploitation, considering fusion algorithms such as Discrete Wavelet Transform and Principal Component Analysis.

Section 3: "Forensic Multimedia Retrieval Techniques"

Chapter 5, "The Role of Penetration Testing in Forensic Multimedia Retrieval Process", proposes techniques for focusing an investigation and targeting potential case information from the vulnerability identification phase, through to the media identification phase. These techniques, oriented to critical infrastructures, are based on penetration testing. Moreover, as the main issue for the digital investigator is the vast array of media in which evidence is stored or transmitted, the chapter proposes a framework of methods flexible and adaptable to the context of an investigation.

Section 4: "Audio and Music Classification and Separation"

Chapter 6, "Classification and Separation of Audio and Music Signals", presents and discusses some algorithms for the classification and separation processes of audio and music signals. The classification algorithms are divided into three categories including approaches in the real-time, frequency domain, and time-frequency distribution. Additionally, the chapter introduces some algorithms for separation and segregation of music and audio signals, like Independent Component Analysis, pitch cancellation, and those based on artificial neural networks.

The editor would like to thank the staff at IntechOpen for the opportunity to work on this book.

> **Eduardo Quevedo Gutiérrez** Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

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Section 1

Content-Based Image

Retrieval and Fusion

Section 1
