The current JPEG steganalysis systems, which include various strategies for feature extraction, have attained outstanding achievements. However, a common shortcoming is that they are always conducted on the entire image and do not take advantage of the content diversity. In addition, compared with a low-dimensional feature set, an appropriate rich model with high-dimensional features can obtain substantial improvement in steganalysis performances. A new steganalysis algorithm based on image segmentation is proposed which enables us to utilize the content characteristics of JPEG images. The given images are segmented to several subimages according to the texture complexity and then high-dimensional steganalysis features of each sort of subimages with the same or close texture complexity are extracted separately to build a classifier. The steganalysis results of the whole image are figured out through a weighted fusing process of all categories of the subimages. Experimental results demonstrate that the proposed method exhibits excellent performances and improves the detection accuracy.