To generate the visual codebook, a step of quantization process is obligatory. Several works have proved the efficiency of sparse coding in feature quantization process of BoW based image representation. Furthermore, it is an important method which encodes the original signal in a sparse signal space. Yet, this method neglects the relationships among features. To reduce the impact of this issue, we suggest in this paper, a Laplacian Tensor sparse coding method, which will aim to profit from the relationship among the local features. Precisely, we propose to apply the similarity of tensor descriptors to create a Laplacian Tensor similarity matrix, which can better present in the same time the closeness of local features in the data space and the topological relationship among the spatially near local descriptors. Moreover, we integrate statistical analysis applied to the local features assigned to each visual word in the pooling step. Our experimental results prove that our method prevails or exceeds existing background results.