A Hybrid of Inference and Stacked Classifiers to Indoor Scenes Classification of RGB-D Images

Abstract

Scene classification makes it easier to semantic scene understanding and aids to further processes and inference, using an assignment of pre-defined classes. Under this motive, we proposed an approach to classify indoor scene objects. The proposed method utilizes a stacked classifier model and refines classification results considering segment consistency. Furthermore, the challenging and messy indoor scene images have been addressed, as dealing daily. Finally, this approach simplicity and affordably obtains desirable classification results.