中文摘要：

为提高物体识别性能，提出了一种基于多稀疏分布特征和最近邻分类的目标识别方法。提取图像的梯度模值和方向特征，构建梯度模值和方向图像，分别对灰度图像、梯度模值图像和梯度方向图像进行稀疏表示，提取稀疏分布特征，得到融合后的多稀疏分布特征，再依据最近邻分类方法进行特征分类，实现物体识别。通过在国际公认的COIL-100和PV0C-2007两个公共测试数据集下进行对比实验，对提出方法的参数选择、鲁棒性和识别性能进行综合评价。实验结果表明，采用提出的方法进行物体识别的识别率高于目前经典的SIFT、SURF和ORB方法，是一种有效的物体识别方法。

英文摘要：

In order to im prove the performance of object recognition , this paper proposed an object recognition method basedon multi-sparse distribution features and nearest neighbor classificatio n. I t extracted the features o f gradient m agnitude and d irection o f im ag e, and constructed gradient m agnitude image and gradien t d ire ctio n image. T h e n , it executed sparse representation on gray im a g e , gradient m agnitude image and gradient d ire c tio n image re sp e ctive ly, to extract sparse d is trib u tio n fe a tu re s,and obtained the m u lti-sparse d is trib u tio n features. F in a lly , it classified the features o f d iffe re n t objects according to nearestneighbor classificatio n m e thod , to realize ob je ct recognition. I t im plem ented experim ents on two in te rn a tio n a l common datasetin c lu d in g COIL-100 and PVOC-2007 , and evaluated com prehensively o f the parameters selectio n, robustness and recognitionperform ance o f the new m ethod. The results show that the new m ethod has h igh er accuracy than three classical methods in cluding S IFT ,SURF and ORB on object recognition , and is avalid object recognition method.