In the field of machine learning and pattern recognition, an alternative to conventional classification is hierarchical classification that exploits hierarchical relations between concepts of interest. To the best of our knowledge, all hierarchical classification methods in the literature are designed to reduce computation complexity without sacrificing too much on accuracy performance. In this work on image classification, we first propose a hierarchical image feature extraction that extracts image feature based on the location of current node in hierarchy to fit the images under current node and to better distinguish its subclasses. As far as we know, such node-dependent feature extraction has not been considered in the literature. Contrary to former hierarchical classification methods that only consider local structure of the hierarchy, we propose a novel cross-level hierarchical classification method that utilizes both global and local concept structures throughout the entire path decision-making process. Our experimental result on two datasets shows that the proposed hierarchical feature extraction combined with our novel hierarchical classification achieves better accuracy performance than conventional non-hierarchical classification methods, and hence conventional hierarchical methods as well.