Texture information is critical to the accuracy of image classification systems. In this paper, we propose a novel descriptor called weighted center symmetric local ternary pattern (WCS-LTP), constructed by using the CS-LTP variance of the local region as an adaptive weight to adjust the contribution of the CSLTP code in histogram calculation. Then, based on the proposed WCS-LTP descriptor, we introduce a new local WCS-LTP feature extraction approach. Compared with conventional local CS-LTP feature, our proposed WCS-LTP feature, which exploits the complementary information of local spatial pattern and local contrast, can better characterize the image local texture. Finally, WCS-LTP feature based sparse coding spatial pyramid matching (ScSPM) representation classification is proposed for image classification. Extensive experimental results demonstrate that the effectiveness of our proposed WCS-LTP feature based ScSPM representation classification algorithm.