哈希图半监督学习方法及其在图像分割中的应用  

Hash Graph Based Semi-supervised Learning Method and Its Application in Image Segmentation

作　　者：张晨光[1,2] 李玉鑑[1] 

机构地区：[1]北京工业大学计算机学院,北京100124 [2]海南大学信息科学技术学院,海口571737

出　　处：《自动化学报》2010年第11期1527-1533,共7页Acta Automatica Sinica

基　　金：国家自然科学基金(60775010);北京工业大学高层次人才建设项目资助~~

摘　　要：图半监督学习(Graph based semi-supervised learning,GSL)方法需要花费大量时间构造一个近邻图,速度比较慢.本文提出了一种哈希图半监督学习(Hash graph based semi-supervised learning,HGSL)方法,该方法通过局部敏感的哈希函数进行近邻搜索,可以有效降低图半监督学习方法所需的构图时间.图像分割实验表明,该方法一方面可以达到更好的分割效果,使分割准确率提高0.47%左右;另一方面可以大幅度减小分割时间,以一幅大小为300像素×800像素的图像为例,分割时间可减少为图半监督学习所需时间的28.5%左右.Graph based semi-supervised learning （GSL） method runs slowly because of the need of much time to construct a neighbor graph. This paper presents a hash graph based semi-supervised learning （HGSL） method, which can search neighbors by locality sensitive hashing function and efficiently reduce the time for GSL to construct a neighbor graph. Image segmentation experiments show that HGSL has an improvement of 0.47 % in average segmenting accuracy, and can greatly reduce the segmenting time, e.g., it takes about 28.5 % of the time for GSL to segment an image with size of 300 × 800.

关 键 词：哈希图半监督学习 图半监督学习 局部敏感的哈希函数 图像分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]