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- W1999823089 abstract "Undersegmentation or oversegmentation is a challenge faced in image segmentation methods, and it is extreme important to determine the optimal number of regions (clusters) of an image in real-world applications. In this study, we introduce an adaptive strategy to do so. The basic idea is to firstly oversegment an image by using the Mean-shift (MS) method, and then segment the obtained oversegmented results by using an evolutionary algorithm. In the second stage, a feature is extracted for each region obtained by the MS method, and a new fitness function is designed to determine the optimal number of clusters. The adaptive approach is applied to a variety of images, and the experimental results show that our method is both efficient and effective for image segmentation." @default.
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- W1999823089 date "2014-06-01" @default.
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- W1999823089 title "Adaptive image segmentation by using mean‐shift and evolutionary optimisation" @default.
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- W1999823089 doi "https://doi.org/10.1049/iet-ipr.2013.0195" @default.
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