LINKMEDIA Research team
Linked media appears today as a major challenge, with numerous potential applications in all areas of multimedia. The strong increase of ubiquitous access to the Internet and the resulting convergence of media on the network open countless opportunities for linked media and reinforce the key role of such a challenge. New applications centered on the notion of linked media are emerging today, such as second screen applications and recommendation services. However, because of the lack of adequate technology, linking related content is mostly deferred to human operators in current applications or to user behavior analysis, e.g., via collaborative filtering, thus indirectly considering the content. This fact severely limits the opportunities offered by a web of media, in terms of creativity, scalability, representativeness and completeness, thus negatively impacting the spread of linked media and the development of innovative services in the Internet of media.
Most of the research effort in automatic multimedia content analysis has been devoted so far to describing and indexing content on which core tasks around information retrieval and recommendation are built to develop multimedia applications. This general philosophy mostly reposes on a vision where documents are considered as isolated entities, i.e., as a basic unit which is indexed or analyzed regardless of other content items and of context. Considering documents in isolation has enabled key progress in content-based analysis and retrieval on a large scale: e.g., design of generic descriptors, efficient techniques for content-based analysis, fast retrieval methodology. But ignoring the links, implicit or explicit, between content items also appears as a rather strong assumption with direct consequences on algorithms and applications, both in terms of performance and in terms of possibilities.