[1]姜英,王延江.REM记忆模型在图像分类识别中的应用[J].智能系统学报,2017,12(03):310-317.[doi:10.11992/tis.201605010]
 JIANG Ying,WANG Yanjiang.Application of REM memory model in image recognition and classification[J].CAAI Transactions on Intelligent Systems,2017,12(03):310-317.[doi:10.11992/tis.201605010]
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REM记忆模型在图像分类识别中的应用(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第12卷
期数:
2017年03期
页码:
310-317
栏目:
学术论文—智能系统
出版日期:
2017-06-25

文章信息/Info

Title:
Application of REM memory model in image recognition and classification
作者:
姜英 王延江
中国石油大学 信息与控制工程学院, 山东 青岛 266580
Author(s):
JIANG Ying WANG Yanjiang
College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China
关键词:
图像识别记忆建模HOG特征LBP特征Bayesian决策
Keywords:
image recognitionmemory modelingHOG featureLBP featureBayesian decision
分类号:
TP391
DOI:
10.11992/tis.201605010
摘要:
尝试将认知心理学中的记忆模型与图像学习识别联系在一起,研究基于REM(retrieving effective from memory)记忆模型的视觉图像存储与识别方法。采用方向梯度直方图(HOG)和局部二进模式(LBP)生成图像特征向量,并对每个特征向量中的每一个分量按概率进行复制,允许错误复制,最后采用Bayesian决策计算被探测图像特征向量与已学习图像集特征向量的平均似然比值,根据该值判断被探测图像是否已学习过。实验结果表明,提出的算法不仅对同一个物体的小幅度旋转图像具有很好的识别效果,同时对同一类别物体图像识别也具有较好的效果,而且其虚报率远远低于其他识别方法。
Abstract:
We attempt to combine a memory model with image learning and recognition and to research the application of the REM model in image recognition and classification. An image feature vector was obtained by histograms of oriented gradients (HOG) and local binary pattern (LBP) operators; every component of a feature vector was copied with a certain probability, allowing for an error-prone copy of the studied vector. Finally, Bayesian decision theory was applied for calculating the average likelihood ratio between the feature vector of the probe image and that of the studied image set. The value of the ratio was used to decide whether the probe image had been studied.Experimental results demonstrate that the proposed method can gain a good recognition effect not only for the classification of the same object with small rotation angles but also for the recognition of the same category object. Moreover, the false rate is far lower than that of other classification methods.

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备注/Memo

备注/Memo:
收稿日期:2016-05-13。
基金项目:国家自然科学基金项目(61271407,61301242);山东省自然科学基金项目(ZR2013FQ015);中央高校基本科研业务费专项资金资助项目(14CX06066A).
作者简介:姜英,女,1985年生,博士研究生,主要研究方向为计算机视觉、认知记忆建模;王延江,男,1966年生,教授,博士生导师,主要研究方向为模式识别与智能信息处理、人脑记忆计算建模以及人脑结构与功能网络连接分析。主持多项国家自然科学基金项目、山东省自然科学基金项目,发表学术论文100余篇,其中被SCI、EI检索40余篇。
通讯作者:王延江.E-mail:yjwang@upc.edu.cn.
更新日期/Last Update: 2017-06-25