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Fast fuzzy clustering of infrared images

机译:红外图像的快速模糊聚类

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Clustering is an important technique for unsupervised image segmentation. The use of fuzzy c-means clustering can provide more information and better partitions than traditional c-means. In image processing, the ability to reduce the precision of the input data and aggregate similar examples can lead to significant data reduction and correspondingly less execution time. This paper discusses brFCM (bit reduction by Fuzzy C-Means), a data reduction fuzzy c-means clustering algorithm. The algorithm is described and several key implementation issues are discussed. Performance speedup and correspondence to a typical FCM implementation are presented from a data set of 172 infrared images. Average speedups of 59 times that of traditional FCM were obtained using brFCM, while producing identical cluster output relative to FCM.
机译:聚类是无监督图像分割的一项重要技术。与传统的c-means相比,模糊c-means聚类的使用可以提供更多的信息和更好的分区。在图像处理中,降低输入数据的精度并聚合相似示例的能力可以导致数据的显着减少和相应地更少的执行时间。本文讨论了brFCM(通过模糊C均值进行位缩减),一种数据归约模糊c均值聚类算法。描述了该算法,并讨论了几个关键的实现问题。从172个红外图像的数据集中显示了性能提高和与典型FCM实现的对应关系。使用brFCM获得的平均加速比传统FCM快59倍,同时产生与FCM相同的群集输出。

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