  A large amount of research on Convolutional Neural Networks has focused on
flat Classification in the multi-class domain. In the real world, many problems
are naturally expressed as problems of hierarchical classification, in which
the classes to be predicted are organized in a hierarchy of classes. In this
paper, we propose a new architecture for hierarchical classification of images,
introducing a stack of deep linear layers with cross-entropy loss functions and
center loss combined. The proposed architecture can extend any neural network
model and simultaneously optimizes loss functions to discover local
hierarchical class relationships and a loss function to discover global
information from the whole class hierarchy while penalizing class hierarchy
violations. We experimentally show that our hierarchical classifier presents
advantages to the traditional classification approaches finding application in
computer vision tasks.
