| Session: | Poster Session |
| Location: | Poster Area |
| Session Time: | Wednesday, June 27, 15:40 - 17:00 |
| Presentation Time: | Wednesday, June 27, 15:40 - 16:40 |
| Presentation: |
Poster
|
| Paper Title: |
A SAAK TRANSFORM APPROACH TO EFFICIENT, SCALABLE AND ROBUST HANDWRITTEN DIGITS RECOGNITION |
| Authors: |
Yueru Chen; University of Southern California, United States | | |
| | Zhuwei Xu; University of Southern California, United States | | |
| | Shanshan Cai; University of Southern California, United States | | |
| | Yujian Lang; University of Southern California, United States | | |
| | C.-C. Jay Kuo; University of Southern California, United States | | |
| Abstract: |
An efficient, scalable and robust approach to the handwritten digits recognition problem based on the Saak transform is proposed in this work. First, multi-stage Saak transforms are used to extract a family of joint spatial-spectral representations of input images. Then, the Saak coefficients are used as features and fed into the SVM classifier for the classification task. In order to control the size of Saak coefficients, we adopt a lossy Saak transform that uses the principal component analysis (PCA) to select a smaller set of transform kernels. The handwritten digits recognition problem is well solved by the convolutional neural network (CNN) such as the LeNet-5. We conduct a comparative study on the performance of the LeNet-5 and the Saak-transform-based solutions in terms of scalability and robustness as well as the efficiency of lossless and lossy Saak transforms under a comparable accuracy level. |