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Efficient region merging method based on classified merging cost

机译:基于分类合并成本的高效区域合并方法

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During image segmentation, the hierarchy queue in the traditional region merging method has lots of redundant links, and it spends too much time on searching and sorting for keeping links in order. These have seriously affected the merging efficiency. Therefore a new region merging method based on quantified merging cost was proposed. It proposed several constraint conditions to shorten the queue length. Those links which should be put in the queue were quantified according to merging costs so that links with nearly the same cost were put in a same category, and their sorting were unnecessary and skipped. Furthermore, the MAP class in STL was utilized to build a two-dimensional dynamic queue, so that the time consuming will be reduced owing to its advantages in searching and auto-sorting. Comparative experiments showed that his method could not only guaranty the segmentation accuracy, but also obviously improve the merging efficiency.
机译:在图像分割过程中,传统区域合并方法中的层次结构队列具有大量冗余链接,并且在搜索和排序上花费太多时间来保持链接有序。这些都严重影响了合并效率。因此,提出了一种新的基于量化合并成本的区域合并方法。它提出了几种约束条件来缩短队列长度。应根据合并成本对应该放入队列中的那些链接进行量化,以便将具有几乎相同成本的链接放在同一类别中,并且不必要对其进行排序并跳过这些链接。此外,利用STL中的MAP类来构建二维动态队列,由于其在搜索和自动排序方面的优势,因此可以减少耗时。对比实验表明,他的方法不仅可以保证分割的准确性,而且可以明显提高合并效率。

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