吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (6): 1909-1917.doi: 10.13229/j.cnki.jdxbgxb201706032

• Orginal Article • Previous Articles     Next Articles

Scene image categorization algorithm based on multi-level features representation

FAN Min1, HAN Qi1, WANG Fen1, SU Xiao-lan2, XU Hao2, WU Song-lin2   

  1. 1.College of Automation, Chongqing University, Chongqing 400044, China;
    2.Chongqing Urban Power Supply Company of State Grid, Chongqing 400015,China
  • Received:2016-09-08 Online:2017-11-20 Published:2017-11-20

Abstract: With the increases in categories, complexity and content of scene images, a categorization algorithm based on multi-level features representation was proposed. First, object attributes based on high-level feature representation were available. Using simple classifiers, the topics of scene images were exported. Then in the same topic, the low-level feature in the image was extracted by the way of fast locality-constrained low rank coding. Meanwhile, in order to reduce the computational complexity, the method of adding local constraint regularization and replacing kernel norm with F-norm in the processing of low rank coding was adopted to achieve detailed understanding of scene images. Achieving scene classification from coarse understanding of object characteristics to detailed analysis of low-level feature, the method can make full use of the progressive and complementary relationship between different features. The experiment results show that better classification effect is obtained.

Key words: computer application, object bank, low rank coding, multi-level features, scene image categorization

CLC Number: 

  • TP391.4
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