Using holistic features for scene classification by combining classifiers

Scene classification is a useful, yet challenging problem in computer vision. Two important tasks for scene classification are the image representation and the choice of the classifier used for decision making. This paper proposes a new technique for scene classification using combined classifiers method. We run two classifiers based on different features: GistCMCT and spatial MCT and combine the output results to obtain the final class. In this way, we improve accuracy, by taking advantage from the qualities of the two descriptors, without increasing the final size of the feature vector. Experimental results on four used datasets demonstrate that the proposed methods could achieve competitive performance against previous methods.