Multimedia applications usually involve a large number of multimedia objects (texts, images, sounds, etc.). An important issue in this context is the specification of spatial and temporal relationships among these objects. In this paper we define such a model, based on a set of spatial and temporal relationships between objects participating in multimedia applications. Our work exploits existing approaches for spatial and temporal relationships. We extend these relationships in order to cover the specific requirements of multimedia applications and we integrate the results in a uniform framework for spatio-temporal composition representation. Another issue is the efficient handling of queries related to the spatio-temporal relationships among the objects during the authoring process. Such queries may be very costly and appropriate indexing schemes are needed so as to handle them efficiently. We propose efficient such schemes, based on multidimensional (spatial) data structures, for large multimedia applications that involve thousands of objects. Evaluation models of the proposed schemes are also presented, as well as hints for the selection of the most appropriate one, according to the multimedia author's requirements.
In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-constraints and the quality of intermediate clustering results in terms of its structural properties. It uses the clustering algorithm and the validity measure as parameters.
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