Using node relationships for hierarchical classification

Tóm tắt:

Hierarchical classification is a computational efficient approach for large-scale image classification. The main challenging issue of this approach is to deal with error propagation. Irrelevant branching decision made at a parent node cannot be corrected at its child nodes in traversing the tree for classification. This paper presents a novel approach to reduce branching error at a node by taking its relative relationship into account. Given a node on the tree, we model each candidate branch by considering classification response of its child nodes, grandchild nodes and their differences with siblings. A maximum margin classifier is then applied to select the most discriminating candidate. Our proposed approach outperforms related approaches on Caltech-256, SUN-397 and ILSVRC2010-1K.

Tác giả: Tien-Dung Mai; Thanh Duc Ngo; Duy-Dinh Le; Duc Anh Duong; Kiem Hoang; Shin’ichi Satoh

Từ khóa: Hierarchical classification, error propagation, node relationship

Tạp chí: In the Proceedings of the International Conference on Image Processing (ICIP), AZ, USA, September 25-28, (2016)

Chỉ số: Thomson ISI, EI, Scorpus.

Link tải: https://ieeexplore.ieee.org/abstract/document/7532410/authors#authors