On fusing the latent deep CNN feature for image classification

  • Xueliang Liu
  • Rongjie Zhang
  • Zhijun Meng
  • Richang Hong
  • Guangcai Liu
Article
Part of the following topical collections:
  1. Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications

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.

Keywords

Image classification Convolutional neural network Late fusion 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (NSFC) under grants 61632007 and 61502139.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Hefei University of TechnologyHefeiChina
  2. 2.Beihang UniversityBeijingChina
  3. 3.Nanjing University of Information Science and TechnologyNanjingChina

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