Yuan Ye, Zhang Wei, Yu Hai, Zhu Zhiliang
IEEE Trans Image Process. 2021;30:7702-7716. doi: 10.1109/TIP.2021.3108403. Epub 2021 Sep 10.
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.
超像素在计算机视觉应用中被广泛使用。大多数现有的超像素方法使用既定标准不加区分地处理所有像素，导致超像素边界的贴合度和规则性不必要地相互抑制。本研究基于之前的一项工作，提出了一种新的分割策略，将图像内容分类为包含物体边界的有意义区域和包括颜色均匀且纹理丰富区域的无意义部分。基于这种分类，我们设计了两个不同的标准来在不同环境中处理像素，以在有意义的内容区域实现高精度的超像素，并保持无意义区域中超像素的规则性。此外，我们在采用颜色特征时添加了一组权重，成功减少了过分割误差。在与几种先进方法的对比实验中，该方法实现的卓越精度和适度紧凑性表明，内容自适应标准有效地减少了边界贴合度和紧凑性之间的权衡。
