Scene image classification based on visual words concatenation of local and global features

In this paper, we present a novel framework for scene image classification, which depends on corresponding visual words concatenation of speeded up robust features (SURF) and Directional binary code (DBC) feature descriptor. Firstly, we use SURF feature descriptor as a local feature descriptor. The local feature descriptor captures very close visual appearance (distinct structure) among their visual contents representation of an image. Secondly, the DBC feature descriptor captures global features, where color-texture features are extracted from entire image. Then, visual words of local and global descriptors are build separately. The concatenated visual words are used to represent the training images and query image. The SVM classifier is used to classify training samples and a query image is classified based on the similarity between histograms of training samples and query image. We carried out experiments using the challenging scene datasets such as MIT scene, UIUC sports event, and MIT indoor scene datasets. The experimental results demonstrate that the proposed method outperforms compared to the existing scene image classification methods.