Fusion of global and local features using KCCA for Automatic Target Recognition

Based on the ideas of feature fusion and Kernel Canonical Correlation Analysis (KCCA), a novel framework for fusing global and local features on Automatic Target Recognition (ATR) algorithm is proposed. Firstly, the feature fusion method based on KCCA is established, then pseudo Zernike moments and Scale Invariant Feature Transform (SIFT) are extracted as global features and local features. K-means algorithm is applied to normalize the local features to obtain the same form as global features. After the fusion of two features, one-against-all 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.