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Flexibility and efficiency of coding, content extraction, and content-based search are key research topics in the field of interactive multimedia. Ongoing ISO MPEG-4 and MPEG-7 activities are targeting standardization to facilitate such services. European COST Telecommunications activities provide a framework for research collaboration. COST 211 bis and COST 211 ter activities have been instrumental in the definition and development of the ITU-T H.261 and H.263 standards for videoconferencing over ISDN and videophony over regular phone lines, respectively. The group has also contributed significantly to the ISO MPEG-4 activities. At present a significant effort of the COST 211 ter group activities is dedicated toward image and video sequence analysis and segmentation-an important technological aspect for the success of emerging object-based MPEG-4 and MPEG-7 multimedia applications. The current work of COST 211 is centered around the test model, called the Analysis Model (AM). The essential feature of the AM is its ability to fuse information from different sources to achieve a high-quality object segmentation. The current information sources are the intermediate results from frame-based (still) color segmentation, motion vector based segmentation, and changedetection-based segmentation. Motion vectors, which form the basis for the motion vector based intermediate segmentation, are estimated from consecutive frames. A recursive shortest spanning tree (RSST) algorithm is used to obtain intermediate color and motion vector based segmentation results. A rule-based region processor fuses the intermediate results; a postprocessor further refines the final segmentation output. The results of the current AM are satisfactory; it is expected that there will be further improvements of the AM within the COST 211 project.

With the proliferation of multimedia data, there is increasing need to support the indexing and searching of high dimensional data. Recently, a vector appro ximationbased technique called VA-le has been proposed for indexing high dimensional data. It has been shown that the VA-le is an e ective technique compared to the current approaches based on space and data partitioning. The VA-le gives good performance especially when the data set is uniformly distributed. Real data sets are not uniformly distributed, are often clustered, and the dimensions of the feature vectors in real data sets are usually correlated. More careful analysis for nonuniform or correlated data is needed for e ectively indexing high dimensional data. We propose a solution to these problems and propose the VA + -le, a new technique for indexing high dimensional data sets based on vector approximations. We conclude with an evaluation of nearest neighbor queries and sho w that the V A + -le technique results in signi cant improvements over the current V A-le approach for sev eral real data sets.

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