Sparse graph-based inductive learning with its application to image classification

Qianying Huang; Xiaohong Zhang; Sheng Huang; Dan Yang

doi:10.1117/1.JEI.25.5.050502

14 September 2016 Sparse graph-based inductive learning with its application to image classification

Qianying Huang, Xiaohong Zhang, Sheng Huang, Dan Yang

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 5, 050502 (September 2016). https://doi.org/10.1117/1.JEI.25.5.050502

Abstract

We present a graph-based classification approach called sparse graph-based inductive learning (SGIL). Different to the conventional graph-based classifiers, which perform the classification in a semisupervised way, SGIL is a purely supervised method whose classifier is totally learned in an inductive fashion instead of transductive fashion. Similar to the idea of sparse graph-based classifier, SGIL constructs a sparse graph to encode the correlations of training samples, and considers the classification issue as a regularized sparse graph partition issue where the optimal graph cut should not only minimize the correlation loss of the training samples but also minimize the classification errors. Essentially, the learned graph cut plays a role as the predicted labels here. Thus, a linear classifier can be inductively derived by learning a mapping between the training samples and the graph cuts. Since SGIL is purely supervised, it enjoys several desirable properties over the semisupervised ones in graph construction and model training. We evaluate our work on several popular image datasets. The experimental results demonstrate its superiority.

Citation Download Citation

Qianying Huang, Xiaohong Zhang, Sheng Huang, and Dan Yang "Sparse graph-based inductive learning with its application to image classification," Journal of Electronic Imaging 25(5), 050502 (14 September 2016). https://doi.org/10.1117/1.JEI.25.5.050502

Published: 14 September 2016

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available