基于二次表示的空间目标图像分类

针对空间目标图像的特点，该文提出一种基于局部不变特征的空间目标图像分类方法。该方法首先提取每幅图像的局部不变特征，利用混合高斯模型(GMM)建立全局的视觉模式，然后依据最大后验概率匹配局部特征和视觉模式来构造整个训练集图像的共现矩阵，采用概率潜在语义分析(PLSA)模型得到图像的潜在类别表示来实现图像的二次表示，最后利用SVM算法实现分类。实验结果验证了该方案的有效性。

Space Target Image Categorization Based on the Second Representation

According to the characteristics of space target image, an novel method of space target image categorization based on local invariant features is proposed. The method extracts firstly local invariant features of each image and uses Gaussian Mixture Model (GMM) to establish global visual modes. Then co-occurrence matrix of the entire training set is constructed by matching local invariant features and visual models with maximum a posteriori probability and Probability Latent Semantic Analysis (PLSA) model is used to obtain latent class vector of images to achieve sencond representation. Finally, the SVM algorithm is used to implement image categorization. The experimental result demonstrates the effectiveness of the proposed method.