吉林大学学报（信息科学版）2012，Vol.30Issue(3)：234-239,6.
融合最小和统计链码与熵矩阵奇异值形状检索
Shape Retrieval Based on Minimum Sum Statistical Chain Code and Entropy Matrix Singular Value
摘要
Abstract
To address the shortcomings of the mere description of the shape of the contour can only describe a simple shape,a fusion of the smallest and statistical chain code and the entropy matrix singular value feature shape classification and retrieval methods,a combination of structure and statistical point of view based on the shape of the contour and regionalshape description, the same time, both the shape of the spatial distribution characteristics. Regional characteristics of the shape extends the spatial distribution characteristics of shape description with the robustness, the shape of the entropy matrix singular value characteristics of the noise stability to make up the chain code to describe the shape characteristics susceptible to noise limitations. Designed and implemented a shape retrieval based on MPEG-7 shape database platform,and experimental results show that the algorithm has a better rotation, translation and scale invariance in shape retrieval and matching.关键词
形状检索/链码/熵矩阵/奇异值Key words
shape retrieval/chain code/entropy matrix/singular value分类
信息技术与安全科学引用本文复制引用
康建玲,李雪妍,郭树旭,张嘉桐..融合最小和统计链码与熵矩阵奇异值形状检索[J].吉林大学学报（信息科学版）,2012,30(3):234-239,6.基金项目
吉林省科技引导计划应用基础研究基金资助项目(20090505) （20090505）
吉林大学自然科学基础研究基金资助项目(450060445135) （450060445135）
