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Subject: Computer Science
Title:
目标识别中的稳定图像特征组合发掘
Alternative Title: the mining of stable image feature-compositions in object recognition
Author: 姜永兵 ; 彭启民
Keyword: frequent item set ; pattern decomposition ; pattern summarization ; stable pattern
Source: 中国图象图形学报
Issued Date: 2012
Volume: 17, Issue:1, Pages:99-105
Department: 中国科学院软件研究所综合信息系统国家级重点实验室;中国科学院研究生院;
Abstract: 针对图像局部特征组合稳定性差和区分力不足的问题,通过对由图像半局部邻域特征挖掘得到的频繁项集进行统计学过滤、模式分解、模式总结及模式组成项间几何关系的建模,提出两种具有较强表征力和区分力的图像中层表示模型:类间共用稳定模式(inter-class common stable pattern)和类内特殊稳定模式(intra-class specialstable pattern)。在将这两种模式引入目标识别框架后,得到了相比同类方法较好的结果。
English Abstract: In order to improve the stability and discrimination of local feature combination for image representation,two image mediate-level representations,Inter-CSP(inter-class common stable pattern) and ntra-SSP(intra-class special stable pattern) are proposed.The details of processing are given,which can be divided into statistic-filtering,pattern decomposition,pattern summarization,and item-based geometric relation modeling on frequent item_sets mined from image semi-local features.A recognition framework is introduced based on Inter-CSP and Intra-SSP.The experiment results demonstrate that these two kinds of patterns are superior to classical methods.
Language: 中文
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/14637
Appears in Collections:综合信息系统技术国家级重点实验室 _期刊论文

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目标识别中的稳定图像特征组合发掘.pdf(696KB)----限制开放 联系获取全文

Recommended Citation:
姜永兵,彭启民. 目标识别中的稳定图像特征组合发掘[J]. 中国图象图形学报,2012-01-01,17(1):99-105.
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