Camera-based Document Image Retrieval System using Local Features - comparing SRIF with LLAH, SIFT, SURF and ORB

被引：0
|
作者：
Dang, Q. B. [1 ]
Le, V. P. [1 ]
Luqman, M. M. [1 ]
Coustaty, M. [1 ]
Tran, C. D. [2 ]
Ogier, J-M. [1 ]
机构：
[1] Univ La Rochelle, Lab L3I, La Rochelle, France
[2] Can Tho Univ, Coll Informat & Commun Technol, Can Tho, Vietnam
来源：
2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR) | 2015年
关键词：
amera-based Document Image Retrieval; local features; indexing.amera-based Document Image Retrieval; indexing.C; EFFICIENT;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present camera-based document retrieval systems using various local features as well as various indexing methods. We employ our recently developed features, named Scale and Rotation Invariant Features (SRIF), which are computed based on geometrical constraints between pairs of nearest points around a keypoint. We compare SRIF with state-of-the-art local features. The experimental results show that SRIF outperforms the state-of-the-art in terms of retrieval time with 90.8 % retrieval accuracy.
引用
收藏
页码：1211 / 1215
页数：5
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