智能系统学报2017，Vol.12Issue(3)：310-317,8.DOI:10.11992/tis.201605010
REM记忆模型在图像分类识别中的应用
Application of REM memory model in image recognition and classification
摘要
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.关键词
图像识别/记忆建模/HOG特征/LBP特征/Bayesian决策Key words
image recognition/memory modeling/HOG feature/LBP feature/Bayesian decision分类
信息技术与安全科学引用本文复制引用
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(3):310-317,8.基金项目
国家自然科学基金项目(61271407,61301242) （61271407,61301242）
山东省自然科学基金项目(ZR2013FQ015) （ZR2013FQ015）
中央高校基本科研业务费专项资金资助项目(14CX06066A). （14CX06066A）
