机构:
Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200030, Peoples R ChinaShanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200030, Peoples R China
Dong, Xiaojie
[1
]
Liu, Erqi
论文数: 0引用数: 0
h-index: 0
机构:
China Aerosp Sci & Ind Corp, Beijing, Peoples R ChinaShanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200030, Peoples R China
Liu, Erqi
[2
]
Yang, Jie
论文数: 0引用数: 0
h-index: 0
机构:
Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200030, Peoples R ChinaShanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200030, Peoples R China
Yang, Jie
[1
]
机构:
[1] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200030, Peoples R China
[2] China Aerosp Sci & Ind Corp, Beijing, Peoples R China
来源:
2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013)
|
2013年
关键词:
Local Descriptor;
Image Matching;
Performance Evaluation;
Subregion;
AFFINE;
REPRESENTATION;
D O I:
暂无
中图分类号:
TB8 [摄影技术];
学科分类号:
0804 ;
摘要:
A novel distinctive descriptor named MSOGH is proposed, which is able to well represent the interest region and is robust to photometric transformations and geometric transformations. According to intensity order, subregions are firstly constructed. Then feature descriptor of the subregion is computed by point permutation of the sample points in each subregion. Finally, feature descriptor of the region is formed by concatenating all subregion feature descriptors. The discriminative power of the proposed descriptor is compared with 5 major existing region descriptors (MROGH, SIFT, GLOH, PCA-SIFT and spin images). Extensive experimental results show that the proposed descriptor achieves better performance than state-of-the-art descriptors.