Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel deep learning framework for object classification called negative-supervised cascaded deep learning. There are two hierarchies in our cascaded method: the first one is a convolutional neural network trained on positive-only samples, which is used to select supervisory samples from a negative library. The second one is inherited from the trained first CNN. It is trained on positive and negative samples, which are selected from domain related database by utilizing negative-supervised mechanism. Experiments are applied this idea to traffic sign classification using two classic convolutional neural networks, LeNet-5 and AlexNet as baselines. Classification rates improved by 0.7% and 1.1% with LeNet-5 and AlexNet respectively, which demonstrates the efficiency and superiority of our proposed framework. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-662-48558-3_25 | Communications in Computer and Information Science |
Keywords | DocType | Volume |
Convolutional neural network,Deep learning,Negative-supervised,Object classification,Traffic sign classification | Conference | 546 |
ISSN | Citations | PageRank |
1865-0929 | 2 | 0.38 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaixuan Xie | 1 | 2 | 0.38 |
| shiming ge | 2 | 4 | 1.76 |
| Rui Yang | 3 | 9 | 2.20 |
| Xiang Lu | 4 | 181 | 16.16 |
| Limin Sun | 5 | 256 | 29.54 |