基于改进CLARA算法的快速图像分割

M. K. Pakhira
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引用次数: 10

摘要

CLARA算法是目前常用的聚类算法之一。该算法在随机选择的原始数据子集上工作,并以比其他聚类算法更快的速度产生接近准确的结果。CLARA主要用于数据挖掘应用程序。我们已经将该算法用于彩色图像分割。为了产生更好的输出,对原来的CLARA进行了修改。我们使用了一种模拟结果的平均技术来减少由于采样造成的误差。由于图像中存在大量的空间相干性,我们将该算法应用于彩色图像的分割。改进后的算法也适用于一般的数据挖掘应用。从实验结果来看,我们所建议的修改是一个更快的CLARA版本,并且能够产生更好的结果。
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Fast Image Segmentation Using Modified CLARA Algorithm
The CLARA algorithm is one of the popular clustering algorithms in use nowadays. This algorithm works on a randomly selected subset of the original data and produces near accurate results at a faster rate than other clustering algorithms. CLARA is basically used in data mining applications. We have used this algorithm for color image segmentation.The original CLARA is modified for producing better outputs. We used a technique of averaging of simulation results to reduce error due to sampling. We applied this algorithm for segmentation of color images due to the large amount of spatial coherency present in the image. The modified algorithm is also suitable for general data mining applications. From experimental results,we see that the suggested modification is a faster version of CLARA as well as able to produce better results.
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