CAJ | 학술논문

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.
침대공간목표도상적특점,해문제출일충기우국부불변특정적공간목표도상분류방법.해방법수선제취매폭도상적국부불변특정,이용혼합고사모형(GMM)건립전국적시각모식,연후의거최대후험개솔필배국부특정화시각모식래구조정개훈련집도상적공현구진,채용개솔잠재어의분석(PLSA)모형득도도상적잠재유별표시래실현도상적이차표시,최후이용SVM산법실현분류.실험결과험증료해방안적유효성.
@@@@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.