Joint Maximum Purity Forest with Application to Image Super-Resolution

TL;DR

This paper introduces the Joint Maximum Purity Forest (JMPF), a novel random-forest method that transforms features into a pre-clustered space to improve classification, clustering, regression, and image super-resolution performance.

Contribution

The paper presents a new random-forest scheme that uses a rotation matrix to create a pre-clustered feature space, enhancing accuracy in various tasks including image super-resolution.

Findings

JMPF outperforms state-of-the-art random-forest methods on benchmark datasets.

JMPF achieves superior results in image super-resolution tasks.

The method effectively handles clustering and regression problems with high accuracy.

Abstract

In this paper, we propose a novel 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 pre-clustered feature space, via a trained rotation matrix. The rotation matrix is obtained through an iterative quantization process, where the input data belonging to different classes are clustered to the respective vertices of the new feature space with maximum purity. In the new feature space, orthogonal hyperplanes, which are employed at the split-nodes of decision trees in random forests, can tackle the clustering problems effectively. 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, because the transformed, compact features are more discriminative to the clustering-regression scheme. Experiment results on several public benchmark datasets also showed that the JMPF-based image super-resolution scheme is consistently superior to recent state-of-the-art image super-resolution algorithms.