Superpixels are widely used in computer vision applications. Most of the existing superpixel methods use established criteria to indiscriminately process all pixels, resulting in superpixel boundary adherence and regularity being unnecessarily inter-inhibitive. This study builds upon a previous work by proposing a new segmentation strategy that classifies image content into meaningful areas containing object boundaries and meaningless parts that include color-homogeneous and texture-rich regions. Based on this classification, we design two distinct criteria to process the pixels in different environments to achieve highly accurate superpixels in content-meaningful areas and keep the regularity of the superpixels in content-meaningless regions. Additionally, we add a group of weights when adopting the color feature, successfully reducing the undersegmentation error. The superior accuracy and the moderate compactness achieved by the proposed method in comparative experiments with several state-of-the-art methods indicate that the content-adaptive criteria efficiently reduce the compromise between boundary adherence and compactness.

中文翻译：

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具有内容自适应标准的超像素


超像素广泛应用于计算机视觉应用中。大多数现有的超像素方法使用既定的标准来不加区别地处理所有像素，导致超像素边界粘附和规律性不必要地相互抑制。这项研究建立在先前工作的基础上，提出了一种新的分割策略，将图像内容分类为包含对象边界的有意义的区域和包含颜色同质和纹理丰富的区域的无意义部分。基于这种分类，我们设计了两种不同的标准来处理不同环境中的像素，以在内容有意义的区域中实现高精度的超像素，并在内容无意义的区域中保持超像素的规律性。此外，我们在采用颜色特征时添加了一组权重，成功地减少了欠分割错误。在与几种最先进方法的比较实验中，所提出的方法实现了卓越的准确性和适度的紧凑性，表明内容自适应标准有效地减少了边界粘附性和紧凑性之间的折衷。