A scene classification method based on ensemble SVM results

Abstract

In classification problem, single classifier may not fully catch the dataset's information. Thus, an ensemble method based on Support Vector Machine (SVM) is proposed in this paper for image scene classification. First, Scale Invariant Feature Transform (SIFT) is used to extract the features of the images, and the SIFT features are clustered to form a visual vocabulary. Then, the SIFT features of each image are compared with this visual vocabulary to calculate the appearance frequencies of the visual words, which consist of the Bag-of-Words (BOW) model descriptions of the image. Probabilistic Latent Semantic Analysis (PLSA) is used to exploit the latent semantic features on the basis of the BOW model, and SVM classifier is then trained by these latent semantic features. These processes repeat N times and N different SVM classifiers are trained. Finally, they are used to classify the testing images, and the ensemble of the N different classification results are calculated as the final result. Experiments show that our method can be effectively applied to the scene classification problem, and the accuracy could be improved with a certain degree of robustness.