论文标题
图像表示的有效图形结构
Efficient graph construction for image representation
论文作者
论文摘要
图可用于解释广泛使用的图像处理方法，例如双边滤波或开发新的图像处理方法，例如基于内核的技术。但是，经常使用简单的图形构造，其中边缘重量和连接取决于一些参数。特别是，图的稀疏性取决于窗口大小的选择。作为替代方案，我们扩展并适应了最近引入非负核回归（NNK）图构造的图像。在NNK图中，稀疏性适应了固有的数据属性。此外，尽管以前的工作在通用设置中考虑了NNK图，但在这里我们开发了利用图像属性的新颖算法，以便NNK方法可以扩展到大图像。我们的实验表明，与使用直接从双边滤波器得出的图相比，稀疏的NNK图可提高能量压实和降解性能。
Graphs are useful to interpret widely used image processing methods, e.g., bilateral filtering, or to develop new ones, e.g., kernel based techniques. However, simple graph constructions are often used, where edge weight and connectivity depend on a few parameters. In particular, the sparsity of the graph is determined by the choice of a window size. As an alternative, we extend and adapt to images recently introduced non negative kernel regression (NNK) graph construction. In NNK graphs sparsity adapts to intrinsic data properties. Moreover, while previous work considered NNK graphs in generic settings, here we develop novel algorithms that take advantage of image properties so that the NNK approach can scale to large images. Our experiments show that sparse NNK graphs achieve improved energy compaction and denoising performance when compared to using graphs directly derived from the bilateral filter.
