Ban Zhihua, Liu Jianguo, Cao Li
IEEE Trans Image Process. 2018 May 16. doi: 10.1109/TIP.2018.2836306.
Superpixel segmentation partitions an image into perceptually coherent segments of similar size, namely, superpixels. It is becoming a fundamental preprocessing step for various computer vision tasks because superpixels significantly reduce the number of inputs and provide a meaningful representation for feature extraction. We present a pixel-related Gaussian mixture model (GMM) to segment images into superpixels. GMM is a weighted sum of Gaussian functions, each one corresponding to a superpixel, to describe the density of each pixel represented by a random variable. Different from previously proposed GMMs, our weights are constant, and Gaussian functions in the sums are subsets of all the Gaussian functions, resulting in segments of similar size and an algorithm of linear complexity with respect to the number of pixels. In addition to the linear complexity, our algorithm is inherently parallel and allows fast execution on multicore systems. During the expectation-maximization iterations of estimating the unknown parameters in the Gaussian functions, we impose two lower bounds to truncate the eigenvalues of the covariance matrices, which enables the proposed algorithm to control the regularity of superpixels. Experiments on a wellknown segmentation dataset show that our method can efficiently produce superpixels that adhere to object boundaries better than the current state-of-the-art methods.
超像素分割将图像分割成大小相似、在感知上连贯的片段，即超像素。它正成为各种计算机视觉任务的一个基本预处理步骤，因为超像素显著减少了输入数量，并为特征提取提供了有意义的表示。我们提出一种与像素相关的高斯混合模型（GMM），将图像分割成超像素。高斯混合模型是高斯函数的加权和，每个高斯函数对应一个超像素，用于描述由随机变量表示的每个像素的密度。与先前提出的高斯混合模型不同，我们的权重是恒定的，并且求和中的高斯函数是所有高斯函数的子集，从而产生大小相似的片段以及一种关于像素数量具有线性复杂度的算法。除了线性复杂度外，我们的算法本质上是并行的，并且能够在多核系统上快速执行。在估计高斯函数中未知参数的期望最大化迭代过程中，我们施加两个下限来截断协方差矩阵的特征值，这使得所提出的算法能够控制超像素的规则性。在一个知名分割数据集上的实验表明，我们的方法能够高效地生成超像素，这些超像素比当前的最先进方法能更好地贴合物体边界。
