A novel similarity measure for curvature scale space descriptor

被引：0

作者：

Peng, Jinye ^{[1
]}

Umemura, Kyoji

Xu, Yinghui

Yang, Wanhai

机构：

[1] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China

[2] Toyohashi Univ Technol, Dept Informat & Comp Sci, Toyohash Aichi 4418580, Japan

[3] NW Univ Xian, Sch Informat & Technol, Xian 710069, Peoples R China

[4] RICOH CO Ltd, Software R & D Grp, Bunkyo Ku, Tokyo 1120002, Japan

来源：

DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS | 2006年 / 13E卷

关键词：

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Shape representation using Curvature scale space descriptor (CSSD), which is adopted as MPEG7 standard, has been widely used in image retrieval. In this paper, a new similarity measure, called Polar Curvature Scale Space (PCSS) measure, is proposed to improve the performance of shape retrieval. In our proposal, the distance between query and gallery images is calculated on a circular vector map, which is converted from the original canonical CSS (CCSS) descriptor space. Comparative experiment by using a marine animal database showed that the PCSS outputs better matching images than that of CCSS in general.

引用

页码：2509 / 2513

页数：5

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