Name
Abstract | ||
|---|---|---|
In this paper, a novel hierarchical deep network is proposed to combine the deep convolutional neural network and the feature selection-based tree classifier efficiently for image classification. First, the concept ontology is built for organizing large-scale image classes hierarchically in a coarse-to-fine fashion. Second, a novel selective orthogonal algorithm is proposed to make sure deep features extracted for each level classifiers more in line with the requirements of different classification tasks. Also, the role of useful feature components in multi-level deep features are improved. The experimental results on three datasets show that adding a feature selection module in a hierarchical deep network can perform better performance in large-scale image classification. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2966651 | IEEE ACCESS |
Keywords | DocType | Volume |
Feature selection,multi-level tree classifiers,image classification,selective orthogonal | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guiqing He | 1 | 0 | 0.34 |
| Jiaqi Ji | 2 | 1 | 2.38 |
| Haixi Zhang | 3 | 0 | 1.69 |
| Yuelei Xu | 4 | 0 | 0.34 |
| Jianping Fan | 5 | 2677 | 192.33 |