论文标题
基于补丁的最近邻居匹配的纹理超级像素聚类
Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching
论文作者
论文摘要
超级像素广泛用于计算机视觉应用中。然而，分解方法仍然无法根据其本地纹理有效地群集图像像素。在本文中，我们提出了一种新的基于邻居的超级像素聚类（NNSC）方法，以在有限的计算时间内生成纹理感知的超类像素，而不是以前的方法。我们使用基于补丁的最近邻居匹配引入了一个新的聚类框架，而大多数现有方法基于像素K-均值聚类。因此，我们直接将贴片空间中的像素分组，以捕获纹理信息。我们通过在标准颜色和纹理数据集上的分割性能方面进行了有利的比较来证明我们的方法的效率。我们还显示了与最近感知的超级像素方法相比，NNSC的计算效率。
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.
