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In recent years, there has been considerable interest in non-standard learning problems, namely in the so-called semi-supervised learning scenarios. Most formulations of semisupervised learning see the problem from one of two (dual) perspectives: supervised learning (namely, classification) with missing labels; unsupervised learning (namely, clustering) with additional information. In this talk, I will review recent work in these two areas, with special emphasis on our own work. For semi-supervised learning of classifiers, I will describe an approach which is able to incorporate unlabelled data as a regularizer for a (maybe kernel) classifier. Unlike previous approaches, the method is non-transductive, thus computationally inexpensive to use on future data. For semisupervised clustering, I will present a new method, which is able to incorporate pairwise prior information in a computationally efficient way. Finally, I will review recent, as well as potential, applications of semi-supervised learning techniques in multimedia problems. [ABSTRACT FROM AUTHOR]

New digital multimedia content is being generated at a tremendous rate. At the same time, the growing variety of distributions channels, e.g., Web, wireless/mobile, cable, IPTV, satellite, is increasing users' expectations for accessibility and searchability of digital multimedia content. However, users are still finding it difficult to find relevant content and indexing & search are not keeping up with the explosion of content. Recent advances in multimedia content analysis are helping to more effectively tag multimedia content to improve searching, retrieval, repurposing and delivering of relevant content. We are currently developing a system called Marvel that uses statistical machine learning techniques and semantic concept ontologies to model, index and search content using audio, speech and visual content. The benefit is a reduction in manual processing for tagging multimedia content and enhanced ability to unlock the value of large multimedia repositories. [ABSTRACT FROM AUTHOR]

For thousands of years, humans have sought means to secretly communicate. Today, ad hoc signaling methods are used in applications as varied as digital rights management for multimedia, content identification, authentication, steganography, transaction tracking, and networking. This talk will present an information-theoretic framework for analyzing such problems and designing provably good signaling schemes. Key ingredients of the framework include models for the signals being communicated and the degradations, jammers, eavesdroppers and codebreakers that may be encountered during transmission. [ABSTRACT FROM AUTHOR]

In this talk I will described issues related to securing multimedia content. In particular I will discuss why tradition security methods, such as cryptography, do not work. I believe that perhaps too has been promised and not enough has been delivered with respect to multimedia security. I will overview research issues related to data hiding, digital rights management systems, media forensics, and describe how various applications scenarios impact security issues. [ABSTRACT FROM AUTHOR]