  In this paper, a level-wise mixture model (LMM) is developed by embedding
visual hierarchy with deep networks to support large-scale visual recognition
(i.e., recognizing thousands or even tens of thousands of object classes), and
a Bayesian approach is used to adapt a pre-trained visual hierarchy
automatically to the improvements of deep features (that are used for image and
object class representation) when more representative deep networks are learned
along the time. Our LMM model can provide an end-to-end approach for jointly
learning: (a) the deep networks to extract more discriminative deep features
for image and object class representation; (b) the tree classifier for
recognizing large numbers of object classes hierarchically; and (c) the visual
hierarchy adaptation for achieving more accurate indexing of large numbers of
object classes hierarchically. By supporting joint learning of the tree
classifier, the deep networks and the visual hierarchy adaptation, our LMM
algorithm can provide an effective approach for controlling inter-level error
propagation effectively, thus it can achieve better accuracy rates on
large-scale visual recognition. Our experiments are carried on ImageNet1K and
ImageNet10K image sets, and our LMM algorithm can achieve very competitive
results on both the accuracy rates and the computation efficiency as compared
with the baseline methods.
