Large data methods for multimedia

This tutorial describes techniques essential for searching the large multimedia databases that are now common on the Internet. There are up to 10 million songs in commercial music catalogues and over 300 million images stored in online photo services such as Flickr. How can we find the music, videos or images we want? How can we organize such large collections: find duplicates, create links between similar documents, extract and annotate semantic structures from complex audiovisual documents? Conventional methods for handling large data sets, such as hashing, get us part of the way, but those methods may not straightforwardly be used for similarity-based matching and retrieval in audiovisual document collections. On the other hand, several elaborate methods from multimedia retrieval are available for semantic document analysis. Unfortunately, those methods generally do not scale for large data sets. Instead, new classes of algorithms combining the best of the two worlds of large data methods and semantic analysis are needed to handle large multimedia databases. Innovative methods such as locality sensitive hashing, which are based on randomized probes, are the new workhorses. This tutorial covers methods for multimedia retrieval on large document collections. Starting with audio retrieval, we describe both the theory (i.e., randomized algorithms for hashing) and the implementation details (how do you store hash values for millions of songs?). A special focus is on how to combine large data methods with semantically meaningful descriptors in order to facilitate efficient similarity-based retrieval. Besides audio, the tutorial also covers image, 3d motion and video retrieval.