ABSTRACT
The rapid growth in the computing and mobile technologies, the Internet, social media, storage mediums and the user-generated e-content have a great propensity towards the worldwide generation of a tremendous amount of multimedia big data of different formats (such as text, audio, video and animation) and this contributes to the ever-booming universal multimedia data sphere. Multimedia big data open ups huge amount of opportunities for multimedia services and multimedia applications. The multimedia analytics have a wide range of applications such as sentiment analysis of social media data, computational health informatics, disaster management systems, multimedia summarization, digital forensics, data journalism, natural sciences and urban computing.
Deep learning techniques are the improvised version of the machine learning technique, and have drastically changed the way in which the computing system deal with multimedia data. The system learns more and to do better analysis as it receives more data. Hence, using deep learning techniques for multimedia data analytics is the next logical step for the better utilization of massive data available. Moreover, recent studies show that using deep learning techniques for multimedia data analytics outperforms traditional machine learning approaches. So this chapter focuses on deep learning models and the applications of corresponding models are discussed.
