Pardeep Kumar (editor) - Machine Learning for Intelligent Multimedia Analytics: Techniques and Applications (Studies in Big Data, 82)

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Recently, multimedia such as text, image, audio, video, graphics stands as one of the most demanding and exciting aspects of the information era. Surveys established that every second a lot of multimedia information are created, published and transmitted explosively, becoming an indispensable part of todays big data. Such large-scale multimedia data has disclosed new challenges and opportunities for intelligent multimedia analysis. It provides innovative opportunities to address several potential research problems, e.g., enabling comprehensive visual classification to fill the semantic gap by exploring large-scale data, offering a promising frontier for detailed multimedia understanding, as well as extract patterns and making effective decisions by analyzing the large collection of data. The processing of multimedia large-scale data has emerged as a most demanding area for the application of machine learning techniques. Therefore, the machine learning for intelligent multimedia analytics is becoming an emerging research area for multimedia research community.

In view of above, this book presents state-of-the-art intelligent techniques and approaches, design, development and innovative use of machine learning techniques for demanding applications of intelligent multimedia analytics. We have provided potential thoughts and methodologies that help senior undergraduate and graduate students, researchers, programmers and industry professionals in creating new knowledge for the future to develop intelligent multimedia analytics-based novel approach for multimedia applications. Further, key role and great importance of machine learning techniques as a mathematical tool is elaborated in the book. A brief and orderly introduction to the chapters is provided in the following. The book contains fifteen chapters.

Chapter presents a multi-modal technique that uses 2D and 3D ear images for secure access. A two-stage, coarse and fine alignment to fit the 3D ear image of the probe to the 3D ear image of the gallery. The probe and gallery image keypoints are compared using feature vectors, where very similar keypoints are used as coarse alignment correspondence points. Once the ear pairs are matched fairly closely, the entire data is finely aligned to compute the matching score. A detailed systematic analysis using a large ear database has been carried out to show the efficiency of the technique proposed.

Chapter presents efficient and low overhead detection of brain diseases using deep learning-based sparse MRI image classification for four different brain diseases like edema, necrosis, enhancing and non-enhancing tumor. The main contribution of the this book chapter lies in utilizing the sparsity of the set of images that reduces the training and inference time of the CNN.

Chapter introduces continual deep learning framework for medical media screening and archival. The primary section of the proposed piece of work helps us to automatically diagnose tuberculosis (TB) from chest X-rays in minimal execution time and memory footprint. Secondarily, medical media screening and analysis would drive the process of automated archival strategy, tagged to self-generated metadata. Gradual self-improvement of the AI engine narrowing down the fuzzy zone between confident positive and confident negative of diagnosis would be the key achievement of proposed continuous deep learning framework.