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A visual word clustering algorithm based on affinity propagation

机译:一种基于亲和传播的视觉词聚类算法

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Because of the weaknesses of k-means clustering would result in deviation of the vocabulary tree structure, we developed an improved vocabulary tree structure based on affinity propagation clustering algorithm. There were three datasets used to test the tasks: the Corel dataset, the LabelMe dataset and the Caltech-101 dataset. The experiments evaluated this new build method for vocabulary tree offers not only incrementally computed sets of vocabulary tree quickly, but gained in retrieval accuracy as well.
机译:由于K-Means聚类的弱点将导致词汇树结构的偏差,我们开发了一种基于亲和传播聚类算法的改进的词汇树结构。使用三个数据集用于测试任务:Corel DataSet,LabelMe数据集和CALTECH-101数据集。该实验评估了这种用于词汇树的新构建方法不仅提供了逐步计算的词汇树集,而且还以检索精度获得。

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