Semantic analysis of real-world images using support vector machine

Digital cameras and thus digital images are now ubiquitous. How to efficiently manage a large amount of images has become important. The semantic analysis of images is an important issue in multimedia processing. Region-based image retrieval systems attempt to reduce the gap between high-level semantics and low-level features by representing images at the object level. Recently, the support vector machine (SVM) has been proposed to solve the classification problem. It can generate a hyperplane to separate two sets of features and provides good generalization performance. In this paper, we propose a novel method which integrates principal component analysis (PCA) and SVM neural networks for analyzing the semantic content of natural images, in which principal component analysis (PCA) is applied to reduce the dimension of features. Experimental results show that the proposed method is capable of analyzing the components of photographs into semantic categories with high accuracy, resulting in photographic analysis that is similar to human perception. The performance of the proposed method is better than that of the traditional radial basis function (RBF) neural network.