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A Novel Fast Affinity Propagation Based Visual Word Clustering Algorithm

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The weaknesses of k-means clustering would result in deviation of the vocabulary tree structure. In this paper, we developed an improved affinity propagation clustering algorithm based vocabulary tree structure. Three datasets were used to test the tasks: the Corel dataset, the LabelMe dataset and the Caltech-101 dataset. The experimental result shows that this new build method for vocabulary tree offers not only compute vocabulary tree set faster, but also improve the retrieval accuracy.
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Keywords: AFFINITY PROPAGATION CLUSTERING; INDEXING STRUCTURE; VISUAL WORD; VOCABULARY TREE

Document Type: Research Article

Publication date: November 1, 2014

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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