In analyzing natural scene images, texture plays an important role because such images are full of various textures. Although texture is crucial information in analyzing natural scene images, the texture segmentation problem is still hard to solve since the texture often exhibit non-uniform statistical characteristics. Although there are several supervised approaches that partition an image according to pre-defined semantic categories, the ever-changing appearances in the natural images make such schemes intractable. To overcome this limitation, we propose a novel unsupervised texture segmentation method for natural images by using the Region-based Markov Random Field (RMRF) model which enforces the spatial coherence between neighbor regions. We introduce the concept of pivot regions which plays a decisive role to incorporate local data interaction. By forcing pivot regions to adhere to initial labels, we make the Markov Random Field evolve fast and precisely. The proposed algorithm based on the pivot regions and the MRF for encapsulating spatial dependencies between neighborhoods yields high performance for the unsupervised segmentation of natural scene images. Quantitative and qualitative evaluations prove that the proposed method achieves comparable results with other algorithms.