To support large-scale visual recognition, it is critical to train a large number of classifiers with high discrimination power. To achieve this task, in this paper a hierarchical visual tree is constructed for organizing a large number of object classes and image concepts according to their inter-concept visual correlations. Based on the hierarchical visual tree, a novel approach is proposed for learning multi-scale group-based dictionary to support discriminative bag-of-visual-words (BoW-based) image representation. In addition, a structural learning approach is developed to enable large-scale classifier training over such hierarchical visual tree. We have also compared the performance of our hierarchical visual tree with traditional label tree over large-scale image collections. © Springer International Publishing Switzerland 2013.
CITATION STYLE
Lei, H., Zhou, N., Mei, K., Dong, P., & Fan, J. (2013). Constructing hierarchical visual tree for discriminative image representation and classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8294 LNCS, pp. 637–648). Springer Verlag. https://doi.org/10.1007/978-3-319-03731-8_59
Mendeley helps you to discover research relevant for your work.