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Background subtraction is important for detecting moving objects in videos. Currently, there are many approaches to performing background subtraction. However, they usually neglect the fact that the background images consist of different objects whose conditions may change frequently. In this paper, a novel hierarchical background model is proposed based on segmented background images. It first segments the background images into several regions by the mean-shift algorithm. Then, a hierarchical model, which consists of the region models and pixel models, is created. The region model is a kind of approximate Gaussian mixture model extracted from the histogram of a specific region. The pixel model is based on the cooccurrence of image variations described by histograms of oriented gradients of pixels in each region. Benefiting from the background segmentation, the region models and pixel models corresponding to different regions can be set to different parameters. The pixel descriptors are calculated only from neighboring pixels belonging to the same object. The experimental results are carried out with a video database to demonstrate the effectiveness, which is applied to both static and dynamic scenes by comparing it with some well-known background subtraction methods.