This paper presents a novel scene classification method using low-level feature and intermediate feature. The purpose of
the proposed method is to improve the performance of scene classification and reduce the labeled data required using the
complementary information between low-level and intermediate feature. The proposed method uses the co-training
algorithm to classify scenes, in which the low-level feature and intermediate feature are two views of co-training
algorithm. For low-level feature, Block Based Gabor Texture (BBGT) feature is extracted to describe the texture
property of images incorporating the spatial layout information. For intermediate feature, Bag Of Word (BOW) feature is
extracted to describe the distribution of local semantic concepts in images based on quantized local descriptors.
Experiment results show that this proposed method has satisfactory classification performances on a large set of 13 categories of complex scenes.