Multimedia search without visual analysis: the value of linguistic and contextual information

This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features

Automated speech and audio analysis for semantic access to multimedia

The deployment and integration of audio processing tools can enhance the semantic annotation of multimedia content, and as a consequence, improve the effectiveness of conceptual access tools. This paper overviews the various ways in which automatic speech and audio analysis can contribute to increased granularity of automatically extracted metadata. A number of techniques will be presented, including the alignment of speech and text resources, large vocabulary speech recognition, key word spotting and speaker classification. The applicability of techniques will be discussed from a media crossing perspective. The added value of the techniques and their potential contribution to the content value chain will be illustrated by the description of two (complementary) demonstrators for browsing broadcast news archives

From media crossing to media mining

This paper reviews how the concept of Media Crossing has contributed to the advancement of the application domain of information access and explores directions for a future research agenda. These will include themes that could help to broaden the scope and to incorporate the concept of medium-crossing in a more general approach that not only uses combinations of medium-specific processing, but that also exploits more abstract medium-independent representations, partly based on the foundational work on statistical language models for information retrieval. Three examples of successful applications of media crossing will be presented, with a focus on the aspects that could be considered a first step towards a generalized form of media mining

Programmable image associative memory using an optical disk and a photorefractive crystal

The optical disk is a computer-addressable binary storage medium with very high capacity. More than 10^10 bits of information can be recorded on a 12-cm-diameter optical disk. The natural two-dimensional format of the data recorded on an optical disk makes this medium particularly attractive for the storage of images and holograms, while parallel access provides a convenient mechanism through which such data may be retrieved. In this paper we discuss a closed-loop optical associative memory based on the optical disk. This system incorporates image correlation, using photorefractive media to compute the best association in a shift-invariant fashion. When presented with a partial or noisy version of one of the images stored on the optical disk, the optical system evolves to a stable state in which those stored images that best match the input are temporally locked in the loop