基于两级层次特征学习的图像分类方法

Guang-hui SONG; Xiao-gang JIN; Gen-lang CHEN; Yan NIE

doi:10.1631/FITEE.1500346

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基于两级层次特征学习的图像分类方法

常规文章 | Updated：2022-05-19

- 基于两级层次特征学习的图像分类方法
- Two-level hierarchical feature learning for image classification
- 信息与电子工程前沿（英文） 2016年17卷第9期页码：897-906
- Affiliations：
  
  1College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  2Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China
  3College of Science and Technology, Ningbo University, Ningbo 315100, China
- Author bio：
  
  E-mail:xiaogangj@cise.zju.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 61379074) and the Zhejiang Provincial Natural Science Foundation of China (Nos. LZ12F02003 and LY15F020035)
- DOI：10.1631/FITEE.1500346
  中图分类号： TP391.4
- 收稿：2015-10-20，
  
  录用：2016-4-10，
  
  纸质出版：2016-09
- Accepted：
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基于两级层次特征学习的图像分类方法[J]. 信息与电子工程前沿（英文）, 2016,17(9):897-906.

Guang-hui SONG, Xiao-gang JIN, Gen-lang CHEN, et al. Two-level hierarchical feature learning for image classification[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(9): 897-906.
基于两级层次特征学习的图像分类方法[J]. 信息与电子工程前沿（英文）, 2016,17(9):897-906. DOI： 10.1631/FITEE.1500346.

Guang-hui SONG, Xiao-gang JIN, Gen-lang CHEN, et al. Two-level hierarchical feature learning for image classification[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(9): 897-906. DOI： 10.1631/FITEE.1500346.

摘要

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。

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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