Semantic extraction of the building images using support vector machines

The image semantic concept is very important and useful for the image retrieval and browsing. The semantic concept of the image can be inferred from low-level features such as color, shape, texture, etc. In this paper, we propose an approach for the building semantic extraction of the scene image using SVM. We select the edge direction histogram and Gabor texture as the discriminative features to realize the image semantic extraction. Experiments have been done by using the standard two-class SVM and one-class SVM and the results obtained are presented. By comparing the experimental results, we conclude that the two-class SVM yields better performance than the one-class SVM. However, the benefit of using one-class SVM is due to its time saving in training. This classifier does not need many versatile negative examples and achieves a high classification accuracy.