Style retrieval from natural images
It has been a challenging task to identify and distinguish between images of different styles. The challenges mainly come from the extraction of high-level image semantic information, and the presence of the associated ambiguity. In this work, we propose a ranking model for style identification. Given training images of different styles, we learn a pointwise ranking model for each style based on random forests. To handle the high dimensionality of visual features and to prevent against possible ambiguity, we further introduce dimension reduction and pruning techniques for our random forests. In our experiments, we provide quantitative evaluation for style categorization in terms of mean square error (MSE) and relative ranking accuracy. Moreover, our visualization and qualitative results support the use of the proposed method for style retrieval of natural images.