Parallel Algorithm Evaluation in the Image and Clustering Processing
AbstractThe increase of the information volume of the image type in the greatest part of the domains asks for the introduction of some storage and efficient recovery methods of the available data due to content. Unfortunately, the progress registered in the field of the multimedia databases with digital images is not remarkable as being outdated by info explosion. The article proposes compression algorithms aiming to reduce the quantity of data necessary to represent an image and the necessary clustering algorithms, namely the k-means algorithm and ISODATA, which were parallelized both from the point of view of the extracted areas and the execution time. The experimental results were obtained by the implementation of the algorithms using the MPI standard and their execution on a cluster. Ill. 12, bibl. 16 (in English; abstracts in English and Lithuanian).
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