The method combines superpixel and maximum interclass variance (OTSU). First, a number of superpixel subregions with different shapes and sizes are generated based on the watershed transform. Then, the superpixels of buildings are merged using the spectral features of buildings, so the first ...
6) minimum interclass variance 最小类内方差 1. Image segmentation algorithm combining minimum interclass variance with region growing; 最小类内方差和区域生长相结合的图像分割法补充资料:最大最小收益决策法 最大最小收益决策法:这是不确定条件下的决策方法之一,它不遵循价值最大化准则,也不考虑各种可能...
A bias correction was derived for the maximum likelihood estimator (MLE) of the intraclass correlation. The bias consisted of two parts: a correction from MLE to the analysis of variance estimator (ANOVA) and the bias of ANOVA. The total possible bias was always negative and depended upon both...
interclass margins. Like the weighted pairwise Fisher’s criteria in [2], one may also define a weighted maximum margin criterion. Due to the page limit, we omit the discussion in this paper. One may use the distance between mean vectors as the distance between classes, i.e. d(C i...
A simple iterative method is developed for computing the maximum likelihood estimates of the components of variance and thereby the intraclass and interclass correlations, under multivariate normal assumptions involving two classes. The method works efficiently for both balanced and unbalanced data and can...
In Otsu's class-based thresholding, maximized interclass variances of objects between foreground and background of the object were used whereas in Kapur's maximum entropy, it maximizes the entropy of self-dissimilar junction between foreground and background. In both these threshold-based segmentation...
In Otsu's class-based thresholding, maximized interclass variances of objects between foreground and background of the object were used whereas in Kapur's maximum entropy, it maximizes the entropy of self-dissimilar junction between foreground and background. In both these threshold-based segmentation...
Linear discriminant analysis (LDA) [2] preserves discriminative information between data of different classes and finds the optimal set of projection vectors by maximizing the ratio between the interclass and intraclass scatters. PCA, LDA, and their variants [5, 6] are not able to reveal the ...
Equivalently, the goal of MDIP is to preserve the intrinsic graph characterizes the interclass compactness and connects each data point with its neighboring points of the same class. Different from Principal component analysis(PCA)that aims to find a linear mapping which preserves total variance by ...
According to the relevant theory, the error of feature extraction mainly comes from two aspects: (1) the variance of the estimated value increases due to the size of the neighborhood constraints; (2) the error of convolution layer parameters causes the deviation of the estimated mean. Generally...