Scene classification based on spatial pyramid representation by superpixel lattices and contextual visual features

Abstract

Natural scene classification is a challenging open problem in computer vision. We present a novel spatial pyramid representation scheme for recognizing scene category. Initially, each image is partitioned into sub-blocks, applying the technology of superpixel lattices segmentation according to a boosted edge learning boundary map, which makes the objects in each sub-block have the integrity-that is, the features in each sub-block are relatively consistent. Then, we extract the dense scale-invariant feature transform features of the images and form the contextual visual feature description. Finally, the image representations are performed by following the methodology of spatial pyramid. The feature descriptions we present include both local structural information and global spatial structural information; therefore, they are more discriminative for scene classification. Experiments demonstrate that the classification rate can achieve about 87.13% on a set of 15 categories of complex scenes.