Joint maximum purity forest with application to image super-resolution

Hailiang Li; Kin-Man Lam; Dong Li

doi:10.1117/1.JEI.27.4.043005

9 July 2018 Joint maximum purity forest with application to image super-resolution

Hailiang Li, Kin-Man Lam, Dong Li

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 4, 043005 (July 2018). https://doi.org/10.1117/1.JEI.27.4.043005

Abstract

We propose a random-forest scheme, namely joint maximum purity forest (JMPF), for classification, clustering, and regression tasks. In the JMPF scheme, the original feature space is transformed into a compactly preclustered feature space, via a trained rotation matrix. The rotation matrix is obtained through an iterative quantization process, where the input data, inclined to different classes, is clustered to the respective vertices of the feature space with maximum purity. In the feature space, orthogonal hyperplanes, which are employed at the split nodes of the decision trees in a random forest, can effectively tackle the clustering problems. We evaluated our proposed method on public benchmark datasets for regression and classification tasks, and experiments showed that JMPF remarkably outperforms other state-of-the-art random-forest-based approaches. Furthermore, we applied JMPF to image super-resolution (SR) specifically because the transformed, compact features are more discriminative to the clustering-regression scheme. Experimental results on several public datasets also showed that the JMPF-based image SR scheme is consistently superior to recent state-of-the-art image SR algorithms.

Citation Download Citation

Hailiang Li, Kin-Man Lam, and Dong Li "Joint maximum purity forest with application to image super-resolution," Journal of Electronic Imaging 27(4), 043005 (9 July 2018). https://doi.org/10.1117/1.JEI.27.4.043005

Received: 22 November 2017; Accepted: 15 June 2018; Published: 9 July 2018

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