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首页> 外文期刊>Journal of signal processing systems for signal, image, and video technology >Unsupervised Texture Segmentation of Natural Scene Images Using Region-based Markov Random Field
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Unsupervised Texture Segmentation of Natural Scene Images Using Region-based Markov Random Field

机译：基于区域马尔可夫随机场的自然场景图像无监督纹理分割

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摘要

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.
机译：在分析自然场景图像时，纹理起着重要作用，因为这样的图像充满了各种纹理。尽管纹理是分析自然场景图像的关键信息，但是纹理分割问题仍然很难解决，因为纹理通常表现出不均匀的统计特征。尽管有几种受监管的方法可以根据预定义的语义类别对图像进行分区，但是自然图像中不断变化的外观使这种方案难以处理。为了克服此限制，我们通过使用基于区域的马尔可夫随机场（RMRF）模型提出了一种新的自然图像无监督纹理分割方法，该模型可增强相邻区域之间的空间相干性。我们介绍枢轴区域的概念，该概念在合并本地数据交互中起着决定性的作用。通过迫使枢轴区域遵守初始标签，我们使马尔可夫随机场快速准确地演化。提出的基于枢纽区域和MRF的算法用于封装邻域之间的空间相关性，对于自然场景图像的无监督分割具有很高的性能。定量和定性评估证明，该方法可与其他算法取得可比的结果。

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