Full Text:   <766>

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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: 2073

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xiao-gang Jin

http://orcid.org/0000-0002-7787-7228

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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.9 P.897-906

http://doi.org/10.1631/FITEE.1500346


Two-level hierarchical feature learning for image classification


Author(s):  Guang-hui Song, Xiao-gang Jin, Gen-lang Chen, Yan Nie

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   xiaogangj@cise.zju.edu.cn

Key Words:  Transfer learning, Feature learning, Deep convolutional neural network, Hierarchical classification, Spectral clustering


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.

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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.

基于两级层次特征学习的图像分类方法

概要:在图像分类任务中,不同类别之间的相似度是不同的,样本经常被误分到相似度较高的类别中。为了区分高度相似类别中的样本,需要更加具体的图像特征,以便于分类器能够提高分类性能。本文提出了一种新颖、有效的基于深度卷积神经网络的两级层次特征学习框架。首先,不同层次的深度特征抽取器使用迁移学习方法进行训练。然后,从全部类别中抽取的通用特征和从高度相似类别中抽取的具体特征被融合成一个特征向量,并将其输入线性分类器进行分类。最后,基于Caltech-256、Oxford Flower-102和Tasmania Coral Point Count三个图像数据集的实验证明,通过两级层次特征学习的深度特征的表达能力十分强大,与传统的扁平多分类方法相比,我们提出的方法能有效地提高分类精度。
关键词:迁移学习;特征学习;深度卷积神经网络;层次分类;谱聚类

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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