基于通用距离测量的机器学习方法用于图像分类和聚类

针对图像分类识别问题,提出了一种用于图像特征提取的新方法.首先定义了基于图像字符串的复杂度和以及通用图像距离(UID),然后依次提出了测量通用图像距离的UID距离测量算法,在维持特征类别之间的固有差异条件下对图像原型进行选择的原型选择算法,利用原型选择算法创建图像的特征向量表示从而生成待分类图像的特征向量的特征向量生成算法,最后基于前述算法提出了对图像的感兴趣区域进行分离的图像分类学习算法.将所提出的方法应用于卫星图像数据的几个监督和非监督学习实验,结果表明文中所提方法效果理想.

Machine learning methods based on universal distance measurement for image classification and clustering

In this paper,a new method for image feature extraction is proposed based on the image classification recognition problem.Firstly defining the complexity of the image string and the universal image distance (UID),and then proposing a UID distance measurement algorithm to measure the general image distance,a prototype selection algorithm for selecting the image prototype under the inherent difference between the maintenance feature categories,a feature vector generation algorithm to generate the eigenvector of the image to be classified by using the prototype selection algorithm to create the feature vector of the image to be classified,and finally an image classification learning algorithm to separate the region of interest of the image based on the aforesaid algorithms.The proposed method is applied to several supervised and unsupervised learning experiments of satellite image data.The results show the feasibility of the proposed method.