The performance of the Bag-of-Words model in the field of image classification is limited mainly by the quantization error of the local feature. To reduce the quantization error of the local feature effectively

an image classification method based on global coding combined with multi-scale codebook is proposed. A global coding is implemented by utilizing fully the manifold structure of the image features and by computing the global information of the codebook. The coding coefficients obtained by the method are relatively smooth and accurate. Furthermore

a multi-path method is designed to integrate all feature representations to describe the image. To a certain extent

this method can achieve the scale invariance of feature representations. Several experiments are conducted on two commonly used benchmark data sets

namely

UIUC-8 and Catltech-101

and the average classification accuracy rates reach up to 88.0% and 83.2%

respectively. Experimental results show that the proposed method improves the performance significantly compared with the fixed-scale locality-constrained coding methods.