Sparse feature convolutional neural network with cluster max extraction for fast object classification

Sung Hee Kim, Dong Sung Pae, Tae Koo Kang, Dong W. Kim, Myo Taeg Lim

Research output: Contribution to journalArticlepeer-review


We propose the Sparse Feature Convolutional Neural Network (SFCNN) to reduce the volume of convolutional neural networks (CNNs). Despite the superior classification performance of CNNs, their enormous network volume requires high computational cost and long processing time, making real-time applications such as online-training difficult. We propose an advanced network that reduces the volume of conventional CNNs by producing a region-based sparse feature map. To produce the sparse feature map, two complementary region-based value extraction methods, cluster max extraction and local value extraction, are proposed. Cluster max is selected as the main function based on experimental results. To evaluate SFCNN, we conduct an experiment with two conventional CNNs. The network trains 59 times faster and tests 81 times faster than the VGG network, with a 1.2% loss of accuracy in multi-class classification using the Caltech101 dataset. In vehicle classification using the GTI Vehicle Image Database, the network trains 88 times faster and tests 94 times faster than the conventional CNNs, with a 0.1% loss of accuracy.

Original languageEnglish
Pages (from-to)2468-2478
Number of pages11
JournalJournal of Electrical Engineering and Technology
Issue number6
Publication statusPublished - 2018 Nov

Bibliographical note

Funding Information:
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B01016071), and (NRF-2016R1D1A1B 03936281), and also (No. 2017R1D1A1B03031467).

Publisher Copyright:
© The Korean Institute of Electrical Engineers.


  • Classification
  • Deep learning
  • Object recognition
  • Online-training control

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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