传统的图像聚类方法存在对初始数据敏感且计算复杂度高的问题,且图像全局特征难以有效地表达图像内容。针对这些问题,提出一种基于Union-Find的图像聚类方法。首先,该方法采用视觉词袋模型Bo VWM(Bag of Visual Words Model)来描述图像...传统的图像聚类方法存在对初始数据敏感且计算复杂度高的问题,且图像全局特征难以有效地表达图像内容。针对这些问题,提出一种基于Union-Find的图像聚类方法。首先,该方法采用视觉词袋模型Bo VWM(Bag of Visual Words Model)来描述图像内容并且利用投票方法来计算每对图像的相似度得分;然后,对于相似度得分大于给定阈值的图像对进行union和find两个操作并将相连的分量形成聚类结果。实验结果表明,该方法较之于传统方法能较好地改善图像聚类效果,且不需要初始聚类数目作为先验参数。展开更多
A fast label-equivalence-based connected components labeling algorithm is proposed in this paper.It is a combination of two existing efficient methods,which are pivotal operations in two-pass connected components labe...A fast label-equivalence-based connected components labeling algorithm is proposed in this paper.It is a combination of two existing efficient methods,which are pivotal operations in two-pass connected components labeling algorithms.One is a fast pixel scan method,and the other is an array-based Union-Find data structure.The scan procedure assigns each foreground pixel a provisional label according to the location of the pixel.That is to say,it labels the foreground pixels following background pixels and foreground pixels in different ways,which greatly reduces the number of neighbor pixel checks.The array-based Union-Find data structure resolves the label equivalences between provisional labels by using only a single array with path compression,and it improves the efficiency of the resolving procedure which is very time-consuming in general label-equivalence-based algorithms.The experiments on various types of images with different sizes show that the proposed algorithm is superior to other labeling approaches for huge images containing many big connected components.展开更多
文摘传统的图像聚类方法存在对初始数据敏感且计算复杂度高的问题,且图像全局特征难以有效地表达图像内容。针对这些问题,提出一种基于Union-Find的图像聚类方法。首先,该方法采用视觉词袋模型Bo VWM(Bag of Visual Words Model)来描述图像内容并且利用投票方法来计算每对图像的相似度得分;然后,对于相似度得分大于给定阈值的图像对进行union和find两个操作并将相连的分量形成聚类结果。实验结果表明,该方法较之于传统方法能较好地改善图像聚类效果,且不需要初始聚类数目作为先验参数。
基金Sponsored by the National Natural Science Foundation of China (Grant No. 81071219)
文摘A fast label-equivalence-based connected components labeling algorithm is proposed in this paper.It is a combination of two existing efficient methods,which are pivotal operations in two-pass connected components labeling algorithms.One is a fast pixel scan method,and the other is an array-based Union-Find data structure.The scan procedure assigns each foreground pixel a provisional label according to the location of the pixel.That is to say,it labels the foreground pixels following background pixels and foreground pixels in different ways,which greatly reduces the number of neighbor pixel checks.The array-based Union-Find data structure resolves the label equivalences between provisional labels by using only a single array with path compression,and it improves the efficiency of the resolving procedure which is very time-consuming in general label-equivalence-based algorithms.The experiments on various types of images with different sizes show that the proposed algorithm is superior to other labeling approaches for huge images containing many big connected components.
