CAJ | 학술논문

针对多视角条件下的图像目标分类问题,提出一种基于压缩感知特征的稀疏识别方法.该方法以原始图像的感知数据为特征描述,将测试样本与训练样本集的压缩感知特征纳入稀疏识别的框架,并通过求解一个l1范数优化问题来获取分类结果.实验表明,该方法不仅有效利用了压缩感知特征的信息冗余性来保证稀疏识别的性能,而且无需进行预处理就能较好地实现多视角图像的目标分类.
침대다시각조건하적도상목표분류문제,제출일충기우압축감지특정적희소식별방법.해방법이원시도상적감지수거위특정묘술,장측시양본여훈련양본집적압축감지특정납입희소식별적광가,병통과구해일개l1범수우화문제래획취분류결과.실험표명,해방법불부유효이용료압축감지특정적신식용여성래보증희소식별적성능,이차무수진행예처리취능교호지실현다시각도상적목표분류.
@@@@Multi-view image target classification is usually difcult. To deal with the problem, we propose a sparse recognition (SR) method with compressed sensing (CS) features. Sensing data of the original image are used as corresponding features. Both the test sample and the training sample set are integrated into an SR framework with their CS features. Classification results can be obtained by solving an l1-norm optimization problem. Experiments show that excellent performance of SR can be obtained by using CS features that retain information redundancy of the original sample. Meanwhile, multi-view image target classification is robust without preprocessing.