Scenic beauty estimation using independent component analysis and support vector machines

The objective in the scenic beauty estimation (SBE) problem is to develop an automatic classification algorithm that matches human subjective ratings. Algorithms such as principal components analysis (PCA) and decision trees (DT) have been applied to this problem with limited success, motivating our search for a better classifier. Since this is obviously a nonlinear classification problem, we applied two nonlinear techniques: independent component analysis (ICA) and support vector machines (SVMs). We evaluated these algorithms on a standard, publicly available data set using a variety of combinations of features. The optimally configured ICA and SVM systems achieved misclassification rates of 33.4% and 32.2% respectively. This is a significant improvement over the best results previously reported on this task: 36.6% for PCA and 43% for DT. Since ambiguity in the features space is a significant problem in this application, these results validate the effectiveness of nonlinear classification techniques.