Analysis Dictionary Learning based Classification: Structure for Robustness
This work addresses robustness in visual classification for applications like image analysis, but it is incremental as it builds on existing dictionary learning methods.
The paper tackles robust classification by integrating a union of subspaces structure into analysis dictionary learning and including a classifier in the formulation, achieving comparable or better performance than state-of-the-art methods in visual classification tasks with reduced computational complexity.
A discriminative structured analysis dictionary is proposed for the classification task. A structure of the union of subspaces (UoS) is integrated into the conventional analysis dictionary learning to enhance the capability of discrimination. A simple classifier is also simultaneously included into the formulated functional to ensure a more complete consistent classification. The solution of the algorithm is efficiently obtained by the linearized alternating direction method of multipliers. Moreover, a distributed structured analysis dictionary learning is also presented to address large scale datasets. It can group-(class-) independently train the structured analysis dictionaries by different machines/cores/threads, and therefore avoid a high computational cost. A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification. Experiments demonstrate that our method achieves a comparable or better performance than the state-of-the-art algorithms in a variety of visual classification tasks. In addition, the training and testing computational complexity are also greatly reduced.