Name
Abstract | ||
|---|---|---|
Image classification, which aims at assigning a semantic category to images, has been extensively studied during the past few years. More recently, convolution neural network arises and has achieved very promising achievement. Compared with traditional feature extraction techniques (e.g., SIFT, HOG, GIST), the convolutional neural network can extract features from image automatically and does not need hand designed features. However, how to further improve the classification algorithm is still challenging in academic research. The latest research on CNN shows that the features extracted from middle layers is representative, which shows a possible way to improve the classification accuracy. Based on the observation, in this paper, we propose a method to fuse the latent features extracted from the middle layers in a CNN to train a more robust classifier. First, we utilize the pretrained CNN models to extract visual features from middle layer. Then, we use supervised learning method to train classifiers for each feature respectively. Finally, we use the late fusion strategy to combine the prediction of these classifiers. We evaluate the proposal with different classification methods under some several images benchmarks, and the results demonstrate that the proposed method can improve the performance effectively. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11280-018-0600-3 | World Wide Web |
Keywords | Field | DocType |
Image classification, Convolutional neural network, Late fusion | Scale-invariant feature transform,Computer science,Convolutional neural network,Supervised learning,Feature extraction,Artificial intelligence,Fuse (electrical),Classifier (linguistics),Contextual image classification,Machine learning | Journal |
Volume | Issue | ISSN |
22 | SP2 | 1573-1413 |
Citations | PageRank | References |
2 | 0.39 | 29 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xueliang Liu | 1 | 76 | 15.56 |
| Rongjie Zhang | 2 | 2 | 0.73 |
| Zhijun Meng | 3 | 30 | 6.37 |
| Richang Hong | 4 | 4791 | 176.47 |
| Guangcan Liu | 5 | 2515 | 76.85 |