In this study, a scene image classification algorithm is proposed which combines Gabor-LBP frequency domain texture features and lexical model semantic features. The frequency domain information which obtained by Gabor transform, the corresponding LBP feature, and semantic features which extracted by the visual Bag Of Words (BOW) package model are fused to realize the classification. In order to verify the algorithm, we use two standard image test datasets to compare and test. The experimental results show that the proposed algorithm has obvious advantages in improving image texture expression, especially for image illumination, rotation, and scale.