The effective unsupervised change - detection method is fundamental in many applications in that the suitable ground - truth information is not always available 由于先验知识并不是很容易获得的,所以在许多实际应用中非监督图象变化检测方法是一种基本方法。
In view of whether it has the stationary class system . it can be divided into supervised automated classification and unsupervised automated clustering 根据是否有固定的类别体系可分为有监督( supervised )的自动归类和无监督( unsupervised )的自动聚类。
This method makes use of the semantic meaning of audio keywords and the confident boundary of unsupervised scene clusters , the structured events are extracted more accurately 该方法充分利用了音频关键字的丰富语义和无监督的场景的可靠边界做到结构事件的准确提取。
Because scenes have much difference in appearance , we adopt unsupervised scene clustering for its universality , which groups video shots with similar visual content into same cluster 对于视频来说,由于存在较大的场景差异,因此采用无监督的场景聚类,达到通用性的目的。
Due to its unsupervised learning ability , clustering has been widely used in numerous applications , such as pattern recognition , image processing , market research and so on 聚类具有无监督学习能力,被广泛应用于多个领域中,如模式识别、数据分析、图像处理以及市场调研等。
The main emphasis of our research is statistical word sense disambiguation , which can be classified into two categories according different discipline methods : supervised and unsupervised 本文研究的重点在于统计词义消歧技术,它根据使用的训练方法的不同可以分为有指导和无指导的两大类。
4 . in order to realize the unsupervised defect detection , this paper designed the method of multi - channel filter merge . the experiments show the feasible and reliable of this method 4 .为了实现无监督图像分割,本文设计了多通道滤波融合的分割方法,并用实验验证了这一方法的可行性和可靠性。
The experimental results show that these two classification methods of multi - sources information fusion can result in better accuracy than that of conventional unsupervised classification method 实验结果表明基于bdset和fdset融合的分类方法比传统的非监督分类方法具有更好的分类效果，有效地提高了分类的精度。
