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.