Structured Analysis Dictionary Learning for Image Classification
This work addresses image classification efficiency and performance for computer vision applications, but it appears incremental as it builds on existing dictionary learning techniques.
The authors tackled image classification by incorporating class structural information into analysis dictionary learning, resulting in comparable or better performance than state-of-the-art methods and greatly reduced computational complexity.
We propose a computationally efficient and high-performance classification algorithm by incorporating class structural information in analysis dictionary learning. To achieve more consistent classification, we associate a class characteristic structure of independent subspaces and impose it on the classification error constrained analysis dictionary learning. 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, our method greatly reduces the training and testing computational complexity.