In recent years, the rapid development of digital technologies, including the low cost of recording, processing, and storing media, and the growth of high-speed communication networks enabling large-scale content sharing, has led to a rapid increase in the availability of multimedia content worldwide. The availability of such content, as well as the increasing user need of analysing and searching into large multimedia collections, increases the demand for the development of advanced search and analytics techniques for big multimedia data. Although multimedia is defined as a combination of different media (e.g., audio, text, video, images etc.) this book mainly focuses on textual, visual, and audiovisual content, which are considered the most characteristic types of multimedia.
In this context, the bigmultimedia data era brings a plethora of challenges to the fields of multimedia mining, analysis, searching, and presentation. These are best described by the Vs of big data: volume, variety, velocity, veracity, variability, value, and visualization. A modernmultimedia search and analytics algorithmand/or systemhas to be able to handle large databases with varying formats at extreme speed, while having to cope with unreliable “ground truth” information and “noisy” conditions. In addition, multimedia analysis and content understanding algorithms based on machine learning and artificial intelligence have to be employed. Further, the interpretation of the content over time may change, leading to a “drifting target” with multimedia content being perceived differently in different times with often low value of data points. Finally, the assessed information needs to be presented in comprehensive and transparent ways to human users.