The locality-constrained linear coding (LLC) algorithm has been developed as an effective means for image classification. This algorithm uses locality linear constraints to encode feature points of the image, achieving higher classification accuracy than that of Spatial Pyramid Matching (SPM), spatial pyramid matching using sparse coding, and other traditional algorithms. However, the LLC algorithm uses only the locality information for the visual words in the dictionary while rarely using mutual information between the neighbouring feature points, causing severe ambiguities. Based on the principle of the locality correlation of images, we propose a new algorithm, named the dual locality-constrained linear coding algorithm (DLLC), which applies the locality information of the feature points and visual words and uses the discriminant information provided by the nearest neighbouring feature points. The experimental results demonstrate that the accuracy of the DLLC algorithm is higher than that of the LLC algorithm, particularly when the image category is large, but the set of dictionary training data is small.