Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (4): 1079-1083.DOI: 10.11772/j.issn.1001-9081.2015.04.1079

Previous Articles     Next Articles

Image matching algorithm based on histogram of gradient angle local feature descriptor

FANG Zhiwen1,2, CAO Zhiguo1, ZHU Lei1   

  1. 1. School of Automation, Huazhong University of Science and Technology, Wuhan Hunan 430074, China;
    2. Department of Energy and Electrical Engineering, Hunan University of Humanities, Science and Technology, Loudi Hunan 417000, China
  • Received:2014-10-20 Revised:2014-12-18 Online:2015-04-10 Published:2015-04-08


方智文1,2, 曹治国1, 朱磊1   

  1. 1. 华中科技大学 自动化学院, 武汉 430074;
    2. 湖南人文科技学院 能源与机电工程系, 湖南 娄底 417000
  • 通讯作者: 曹治国
  • 作者简介:方智文(1983-),男,湖南长沙人,讲师,博士,主要研究方向:图像处理、机器学习; 曹治国(1964-),男,湖北武汉人,教授,博士,主要研究方向:自动目标识别、机器学习; 朱磊(1982-),男,湖北武汉人,讲师,博士,主要研究方向:图像处理、机器学习。
  • 基金资助:



In order to solve the problem that it is difficult to leverage the performances of effect and efficiency, an image matching algorithm based on the Histogram of Gradient Angle (HGA) was proposed. After obtaining the key points by Features from Accelerated Segment Test (FAST), the block gradient and the new structure as dartboards were introduced to descript the local structure feature. The image matching algorithm based on HGA can work against the rotation, blur and luminance and overcome the affine partly. The experimental results, compared with Speeded Up Robust Feature (SURF), Scale Invariant Feature Transform (SIFT) and ORB (Oriented FAST and Rotated Binary Robust Independent Elementary Features (BRIEF)) in the complex scenes, demonstrate that the performance of HGA is better than other descriptors. Additionally, HGA achieves an accuracy of over 94.5% with only 1/3 of the time consumption of SIFT.

Key words: Histogram of Gradient Angle (HGA), local descriptor, multi-degree of freedom, structure information, image matching



关键词: 角度直方图, 局部特征描述子, 多自由度, 结构特征, 图像匹配

CLC Number: