吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (2): 601-608.doi: 10.13229/j.cnki.jdxbgxb201702035

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Image feature description method based on local entropy

ZHOU Bao-yu1, ZHAO Hong-wei1, XIAO Yang2, ZANG Xue-bai1   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.College of Electronic Information,Jilin Communications Polytechnic, Changchun 130012, China
  • Received:2015-12-22 Online:2017-03-20 Published:2017-03-20

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Abstract: The traditional model, local scale-invariant features, does not capture some parts of the important information of an image, which will lead to bad matching result. In order to address this problem, extracting feature information from different scales in blurring space is better than the classic algorithm. A local entropy method can estimate interesting point information from its surrounding, which can obtain more image content. This paper provides a new image feature description based on local entropy. First, the orient histograms from different scales within different Gaussians space are computed, and each local entropy value is estimated from each scale level. Then, all of the descriptors of the same interesting point are fussed based on the ratio of each entropy to the whole local entropy value of the same feature in different scale levels. Experiment results demonstrate that, compared with order out-of-state local descriptor, the two descriptors provided in this paper have strong robust under different conditions.

Key words: computer application, image feature detection, image feature description, local entropy

CLC Number: 

  • TP391
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