Data Mining and Machine Learning in Multimedia Databases

Dear Colleagues,

Nowadays, thanks to the worldwide availability of cheap information-sensing devices (such as sensors, cameras, RFID readers, and mobile phones) and the growth of storage capacity, data generation has greatly increased, reaching several exabytes per day. Most of such data are of multimedia (MM) types, given the diffusion of inexpensive tools for creating/capturing images, videos, audio, textual documents, and so on.

This MM data avalanche has completely overrun existing techniques for extracting knowledge and value from conventional data. Automatic analysis of MM data is yet an open research issue due to their very complex nature and the lack of appropriate methodologies for accurate and efficient characterization of their content and semantics. Possible contexts of application include, among the others, smart cities, smart mobility, internet-of-things, public health, aging, public/citizen safety/security, advanced education, smart advertising, and automated industry.

This Special Issue focuses on data mining (DM) and machine learning (ML) techniques in the context of MM databases. Our aim is to collect the most recent evidence of innovation in extracting knowledge and value from MM data. We would like to gather researchers from different disciplines and methodological backgrounds to discuss new ideas, original research, recent results, and future challenges in this exciting area. Potential topics include, but are not limited to, the following:

• Big data techniques for MM databases;
• Real-time analysis of massive MM data streams;
• Pipelines for MM data analysis;
• Bias in ML for MM data;
• MM data-driven decision making;
• Classification of MM data;
• Clustering of MM data;
• Prediction of MM data;
• Recommendation of MM data

Prof. Dr. Ilaria Bartolini
Guest Editor

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