  Compared with single-label image classification, multi-label image
classification is more practical and challenging. Some recent studies attempted
to leverage the semantic information of categories for improving multi-label
image classification performance. However, these semantic-based methods only
take semantic information as type of complements for visual representation
without further exploitation. In this paper, we present an innovative path
towards the solution of the multi-label image classification which considers it
as a dictionary learning task. A novel end-to-end model named Deep Semantic
Dictionary Learning (DSDL) is designed. In DSDL, an auto-encoder is applied to
generate the semantic dictionary from class-level semantics and then such
dictionary is utilized for representing the visual features extracted by
Convolutional Neural Network (CNN) with label embeddings. The DSDL provides a
simple but elegant way to exploit and reconcile the label, semantic and visual
spaces simultaneously via conducting the dictionary learning among them.
Moreover, inspired by iterative optimization of traditional dictionary
learning, we further devise a novel training strategy named Alternately
Parameters Update Strategy (APUS) for optimizing DSDL, which alternately
optimizes the representation coefficients and the semantic dictionary in
forward and backward propagation. Extensive experimental results on three
popular benchmarks demonstrate that our method achieves promising performances
in comparison with the state-of-the-arts. Our codes and models have been
released at {https://github.com/ZFT-CQU/DSDL}.
