论文笔记：Efficient Graph-Based Image Segmentation

前言

Graph-Based Segmentation 是经典的图像分割算法，作者Felzenszwalb也是提出DPM算法的大牛。该算法是基于图的贪心聚类算法，实现简单，速度比较快，精度也还行。不过，目前直接用它做分割的应该比较少，毕竟是99年的跨世纪元老，但是很多算法用它作垫脚石，比如Object Propose的开山之作《Segmentation as Selective Search for Object Recognition》就用它来产生过分割（oversegmentation）。还有的语义分割(senmatic segmentation )算法用它来产生超像素

Introduction

对于一个优秀分割算法来说，通常要拥有如下两条性质

1. 算法首先必须要能捕捉到可以反应图像全局特征的region，并且我们可以清楚知道算法在做什么，以及为什么这么做

Capture perceptually important groupings or regions, which often reflect global aspects of the image. Two central issues are to provide precise characterizations of what is perceptually important, and to be able to specify what a given seg- mentation technique does. We believe that there should be precise definitions of the properties of a resulting segmentation, in order to better understand the method as well as to facilitate the comparison of different approaches

2. 算法效率一定要高，必须以接近线性复杂度，这样才能将算法运用在video等实时性较高的application中

Be highly efficient, running in time nearly linear in the number of image pixels. In order to be of practical use, we believe that segmentation methods should run at speeds similar to edge detection or other low-level visual processing techniques, meaning nearly linear time and with low constant factors. For example, a segmentation technique that ru