In this paper the classicist and the connectionist debate is integrated in a multimedia data mining framework. Due to the ready availability of visual information digitally, the problem to store, retrieve and organize images is gaining tremendous importance and poses a serious challenge for practitioners of AI and databases alike. The central problem for image database management is the idea of...

This paper proposes a new methodology to automatically build semantic hierarchies suitable for image annotation and classification. The building of the hierarchy is based on a new measure of semantic similarity. The proposed measure incorporates several sources of information : visual, conceptual and contextual as we defined in this paper. The aim is to provide a measure that best represents im...

Medical images annotation is most of the time a repetitive hard task. Collecting old similar annotations and assigning them to new medical images may not only enhance the annotation process, but also reduce ambiguity caused by repetitive annotations. The goal of this work is to propose an approach based on Hausdorff distance able to compute similarity between a new medical image and old stored ...

The authors have developed the Internet browser application Gymn@zilla for L2-learning. In this paper they present a dynamical image annotation: Gymn@zilla automatically provides explanatory images drawn from Internet for words in L2. The Internet-images are often photos of daily events. The authors discuss the psycholinguistic implications of images in L2 learning in general and specifically t...

In this paper the methods we used in the 2005 ImageCLEF content-based image retrieval evaluation are described. For the medical retrieval task, we combined several low-level image features with textual information retrieval. Combining these two information sources, clear improvements over the use of one of these sources alone are possible. Additionally we participated in the automatic annotatio...

We propose a concept-centered approach that combines regionand image-level analysis for automatic image annotation (AIA). At the region level, we group regions into separate concept groups and perform conceptcentered region clustering separately. The key idea is that we make use of the interand intra-concept region distribution to eliminate unreliable region clusters and identify the main regio...

This paper addresses the automatic analysis of visual content and extraction of metadata beyond pure visual descriptors. Two approaches are described: Automatic Image Annotation (AIA) and Confidence Clustering (CC). AIA attempts to automatically classify images based on two binary classifiers and is designed for the consumer electronics domain. Contrastingly, the CC approach does not attempt to...