Collaborative Representation Graph for Semi-Supervised Image Classification

Junjun GUO  Zhiyong LI  Jianjun MU  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E98-A   No.8   pp.1871-1874
Publication Date: 2015/08/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E98.A.1871
Type of Manuscript: LETTER
Category: Image
image classification,  collaborative representation,  semi-supervised learning,  graph construction,  

Full Text: PDF(458.6KB)>>
Buy this Article

In this letter, a novel collaborative representation graph based on the local and global consistency label propagation method, denoted as CRLGC, is proposed. The collaborative representation graph is used to reduce the cost time in obtaining the graph which evaluates the similarity of samples. Considering the lacking of labeled samples in real applications, a semi-supervised label propagation method is utilized to transmit the labels from the labeled samples to the unlabeled samples. Experimental results on three image data sets have demonstrated that the proposed method provides the best accuracies in most times when compared with other traditional graph-based semi-supervised classification methods.