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In this paper we consider the problem of automatically annotating images with keywords. We first discuss performance measures for the problem in some length. We propose a new information-theory based measure de-symmetrised mutual information (DTMI). We then describe a straightforward solution to the annotation problem. We first train a set of classifiers to detect the presence of each individual keyword...
In late 2004, a new method of publishing multimedia broadcasts on the Internet became popular called 'Podcasting'. Podcasting incorporates existing feed description formats, namely RSS 2.0 (Really Simple Syndication), to deliver various enclosed files which allows users to subscribe to feeds, receiving updates periodically. Originally intended for self-publishing and syndication of audio files, usage...
The endoscopic capsule is a recent technological breakthrough with high clinical importance. Exam analysis duration is its main setback, requiring an average of two hours from a trained specialist. Automation is required and this paper presents a topographic segmentation tool using low-level features that can reduce annotation times up to 15 minutes per exam. This is accomplished using Bayesian classifiers...
A drum sound equalizer, called INTER:D, is described that enables a listener to control the volume and timbre of bass and snare drum sounds in commercial compact-disc recordings. Although the characteristics of the drum sounds are often closely related to the impression made by a musical piece, conventional graphic equalizers cannot adjust their characteristics because they have volume sliders only...
The Semantic Web requires document annotation with various meta-data. But for end-users, doing it manually would be extremely time consuming and unfeasible for billion of documents. To reduce this burden, Information Extraction techniques should be applied. This paper describes the use of a recent probabilistic sequence model, Conditional Random Fields, to annotate semi-automatically sets of documents...
In this paper, we introduce an information theoretic concept to image retrieval by first developing a novel method to compute the amount of information contained in image parts, and then using the informative-ness of the image parts to develop a flexible information selective image retrieval (ISIR) technology. We present experimental results which show that the new method is effective and is especially...
This paper presents a user-centered and holistic approach in order to make large and networked information spaces semantically available and researchable by including human use and interpretation. Application areas are specialized document collections such as intranets, complex portal sites and knowledge databases. The approach combines conventional full-text search with semantic retrieval methods...
Modern database structures consisting of audio visual and low-level semantic data are in line with the needs and requirements of end user customers. It is difficult to expect the end user to provide any high level input rather than a very general key word annotation of the recorded data. A typical user scenario appearing in modern multimedia deals with having just audio-visual data with no additional...
The SemanticHIFI project aims at designing and prototyping tomorrow's Hi-fi systems, which will provide music lovers with innovative functions of management and manipulation of musical contents. The limitations of current equipments are mainly related to those of the music distribution media (album-based audio recordings in stereo format), with poor control features and interfaces (album/ track selection,...
Multimedia data has a rich and complex structure in terms of inter- and intra-document references. Its potential is severely limited unless effective methods for semantic extraction and semantic-based cross-media exploration and retrieval can be devised. Today's leading-edge techniques in this area are working well for low-level feature extraction and, to a certain degree, for concept recognition...
The SIMAC project addresses the study and development of innovative components for a music information retrieval system. The key feature is the usage and exploitation of semantic descriptors of musical content that are automatically extracted from music audio files. These descriptors are generated in two ways: as derivations and combinations of lower-level descriptors and as generalizations induced...
The BOEMIE project proposes a bootstrapping approach to knowledge acquisition, which uses multimedia ontologies for fused extraction of semantics from multiple modalities, and feeds back the extracted information, aiming to automate the ontology evolution process.
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