Sparse Representation Based Complete Kernel Marginal Fisher Analysis Framework for Computational Art Painting Categorization

www.PosterPresentations.com • This paper presents a sparse representation based complete kernel marginal Fisher analysis (SCMFA) framework for categorizing fine art images. • First, we introduce several Fisher vector based features for feature extraction so as to extract and encode important discriminatory information of the painting image. • Second, we propose a complete marginal Fisher analysis method so as to extract two kinds of discriminant information, regular and irregular. • In particular, the regular discriminant features are extracted from the range space of the intraclass compactness using the marginal Fisher discriminant criterion whereas the irregular discriminant features are extracted from the null space of the intraclass compactness using the marginal interclass separability criterion. • Experimental results on the challenging Painting-91 dataset show that our framework achieves the state-of-the-art performance for fine art painting categorization and outperforms other popular image descriptors and deep learning methods INTRODUCTION