Superpixel segmentation based structural scene recognition

Abstract

This paper presents a novel structural model based scene recognition method. In order to resolve regular grid image division methods which cause low content discriminability for scene recognition in previous methods, we partition an image into a pre-defined set of regions by superpixel segmentation. And then classification is modelled by introducing a structural model which has the capability of organizing unordered features of image patches. In the implementation, CENTRIST which is robust to scene recognition is used as original image feature, and bag-of-words representation is used to capture the local appearances of an image. In addition, we incorporate adjacent superpixel's differences as edge features. Our models are trained using structural SVM. Two state-of-the-art scene datasets are adopted to evaluate the proposed method. The experiment results show that the recognition accuracy is significantly improved by the proposed method.