CLC number: TP391.4
On-line Access: 2016-08-31
Received: 2015-10-20
Revision Accepted: 2016-04-10
Crosschecked: 2016-08-08
Cited: 1
Clicked: 7374
Guang-hui Song, Xiao-gang Jin, Gen-lang Chen, Yan Nie. Two-level hierarchical feature learning for image classification[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(9): 897-906.
@article{title="Two-level hierarchical feature learning for image classification",
author="Guang-hui Song, Xiao-gang Jin, Gen-lang Chen, Yan Nie",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="9",
pages="897-906",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500346"
}
%0 Journal Article
%T Two-level hierarchical feature learning for image classification
%A Guang-hui Song
%A Xiao-gang Jin
%A Gen-lang Chen
%A Yan Nie
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 9
%P 897-906
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%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500346
TY - JOUR
T1 - Two-level hierarchical feature learning for image classification
A1 - Guang-hui Song
A1 - Xiao-gang Jin
A1 - Gen-lang Chen
A1 - Yan Nie
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 9
SP - 897
EP - 906
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500346
Abstract: In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network (CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count (CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.
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