一种KCCA融合局部特征和全局特征的目标识别算法

提出一种新的KCCA特征融合算法。首先分别提取目标图像的局部特征SIFT和全局Pseudo-Zernike矩特征，并利用K-means算法对局部特征进行预处理；然后利用KCCA将两种特征提取相关特征进行融合，最后将融合特征送入SVM分类器。对遥感飞机图像库做了分类识别的仿真实验。相比于单一特征和CCA特征融合的识别策略，KCCA识别率得到明显提高，理论分析和实验结果证实了该算法具有良好的准确性与可靠性，能够有效提高图像分类识别系统的准确度。

A Fusing Local Feature and Global Feature Target Recognition Algorithm Based on KCCA

A novel feature fusion algorithm based on KCCA is established.Firstly,scale invariant feature transform (SIFT) and Pseudo Zernike moments are extracted as global features and local features.Then K-means algorithm is applied to normalize the local features to obtain the same form as global features.After the fusion of two features,support vector machine(SVM) is employed as classifier for the multi-class target recognition.Theoretical analysis and experiments on aircraft images results show that KCCA features fusion representations significantly outperform CCA fusion method and single feature approach.Feature fusion of global features and local features based on target image for recognition are proved to be a promising strategy in object recognition field.