基于块最近邻匹配的纹理超像素聚类
Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching
AI总结：
本文提出NNSC方法，利用基于块的最近邻匹配替代逐像素K均值聚类，在有限时间内生成纹理感知超像素，并在标准数据集上验证了其分割性能与计算效率。
摘要：
超像素在计算机视觉应用中广泛使用。然而，分解方法仍可能无法根据局部纹理高效地聚类图像像素。本文提出一种新的基于最近邻的超像素聚类（NNSC）方法，与先前方法相比，能在有限计算时间内生成纹理感知的超像素。我们引入一种基于块最近邻匹配的新聚类框架，而大多数现有方法基于逐像素K均值聚类。因此，我们直接在块空间中对像素进行分组，从而捕获纹理信息。我们通过在标准彩色和纹理数据集上的分割性能对比，证明了我们方法的有效性。我们还展示了NNSC相对于近期纹理感知超像素方法的计算效率。
英文摘要：
Superpixels are widely used in computer vision applications. Nevertheless, decomposition methods may still fail to efficiently cluster image pixels according to their local texture. In this paper, we propose a new Nearest Neighbor-based Superpixel Clustering (NNSC) method to generate texture-aware superpixels in a limited computational time compared to previous approaches. We introduce a new clustering framework using patch-based nearest neighbor matching, while most existing methods are based on a pixel-wise K-means clustering. Therefore, we directly group pixels in the patch space enabling to capture texture information. We demonstrate the efficiency of our method with favorable comparison in terms of segmentation performances on both standard color and texture datasets. We also show the computational efficiency of NNSC compared to recent texture-aware superpixel methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
