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. INTRODUCTION The United States Forest Service (USFS) has a long-term interest in the development of automatic methods [1] for managing forest resources. These methods use a database of forestry images to determine the utility of a plot of forest land both in terms of timber use and scenic quality. Traditional methods used to determine the scenic quality are very tedious and involve a large group of people manually rating each of the images. Clearly, an automatic method has advantages that include consistency and efficiency. To facilitate this research, an extensive database of images [2] has been developed, along with a comprehensive evaluation paradigm. A wide array of features were extracted from these images, and several techniques, ranging from Principal Components Analysis (PCA) to Decision Trees (DT) were used to combine these fea tures . Unfortunately, despite the incorporation of such powerful statistical measures, overall performance [3] on this task was not much better than chance. ALGORITHM DESCRIPTION Linear classifiers [4] are often used due to the simplicity of implementation, and their robustness to poorly estimated statistical models. A linear classifier uses a discriminant function that can be represented as: