文章基本信息

标题：MMKK++ algorithm for clustering heterogeneous images into an unknown number of clusters
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作者：Dávid Papp ; Gábor Szűcs
期刊名称：ELCVIA: electronic letters on computer vision and image analysis
印刷版ISSN：1577-5097
出版年度：2018
卷号：16
期号：3
页码：30-45
DOI：10.5565/rev/elcvia.1054
语种：English
出版社：Centre de Visió per Computador
摘要：In this paper we present a suggested automatic clustering procedure with the main aim to predict the number of clusters of unknown, heterogeneous images. We used the state-of-the-art Fisher-vector for mathematical representation of the images and these vectors were considered as input data points for the clustering algorithm. We implemented a novel variant of K-means, the kernel K-means++, furthermore the min-max kernel K-means plusplus (MMKK++) as clustering method. The proposed approach examines some candidate cluster numbers and uses the law of large numbers in order to choose the optimal cluster size. We conducted experiments on four image sets to demonstrate the efficiency of our solution. The first two image sets are subsets of different popular collections; the third is their union; the fourth is the complete Caltech101 image set.
其他摘要：In this paper we present a suggested automatic clustering procedure with the main aim to predict the number of clusters of unknown, heterogeneous images. We used the state-of-the-art Fisher-vector for mathematical representation of the images and these vectors were considered as input data points for the clustering algorithm. We implemented a novel variant of K-means, the kernel K-means++, furthermore the min-max kernel K-means plusplus (MMKK++) as clustering method. The proposed approach examines some candidate cluster numbers and uses the law of large numbers in order to choose the optimal cluster size. We conducted experiments on four image sets to demonstrate the efficiency of our solution. The first two image sets are subsets of different popular collections; the third is their union; the fourth is the complete Caltech101 image set.