Name
Abstract | ||
|---|---|---|
In recent years, with the raise of the neural network and deep learning, significant progress has been achieved in the field of image recognition. Convolutional Neural Network (CNN) has been widely used in multiple image recognition tasks, but the recognition accuracy still has a lot of room for improvement. In this paper, we proposed a hybrid model CNN-GRNN to improve recognition accuracy. The model uses CNN to extract multilayer image representation and it uses General Regression Neural Network (GRNN) to classify image using the extracted feature. The CNN-GRNN model replace Back propagation (BP) neural network inside CNN with GRNN to improve generalization and robustness of CNN. Furthermore, we validate our model on the Oxford-IIIT Pet Dataset database and the Keck Gesture Dataset, the experiment result indicate that our model is superior to Gray Level Co-occurrency (GLCM),HU invariant moments, CNN and CNN_SVM on small sample dataset. Our model has favorable real-time characteristic at the same time. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.jvcir.2018.07.011 | Journal of Visual Communication and Image Representation |
Keywords | Field | DocType |
Image recognition,Convolutional Neural Network (CNN),General Regression Neural Network (GRNN),Small sample,Real-time | Computer vision,Pattern recognition,Convolutional neural network,Support vector machine,Image representation,Robustness (computer science),Invariant (mathematics),Artificial intelligence,Deep learning,Backpropagation,Artificial neural network,Mathematics | Journal |
Volume | ISSN | Citations |
55 | 1047-3203 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiajia Zhang | 1 | 26 | 16.64 |
| kun shao | 2 | 55 | 7.39 |
| Xing Luo | 3 | 0 | 0.34 |